This presentation provides an overview of Cloudera and how a modern platform for Machine Learning and Analytics better enables a data-driven enterprise.
GENERATIVE AI, THE FUTURE OF PRODUCTIVITYAndre Muscat
Discuss the impact and opportunity of using Generative AI to support your development and creative teams
* Explore business challenges in content creation
* Cost-per-unit of different types of content
* Use AI to reduce cost-per-unit
* New partnerships being formed that will have a material impact on the way we search and engage with content
Part 4 of a 9 Part Research Series named "What matters in AI" published on www.andremuscat.com
Intelligent Agent PPT ON SLIDESHARE IN ARTIFICIAL INTELLIGENCEKhushboo Pal
n artificial intelligence, an intelligent agent (IA) is an autonomous entity which acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors and consequent actuators (i.e. it is intelligent).An intelligent agent is a program that can make decisions or perform a service based on its environment, user input and experiences. These programs can be used to autonomously gather information on a regular, programmed schedule or when prompted by the user in real time. Intelligent agents may also be referred to as a bot, which is short for robot.Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Examples of intelligent agents
AI assistants, like Alexa and Siri, are examples of intelligent agents as they use sensors to perceive a request made by the user and the automatically collect data from the internet without the user's help. They can be used to gather information about its perceived environment such as weather and time.
Infogate is another example of an intelligent agent, which alerts users about news based on specified topics of interest.
Autonomous vehicles could also be considered intelligent agents as they use sensors, GPS and cameras to make reactive decisions based on the environment to maneuver through traffic.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Explore Microsoft Power Platform Center of ExcellenceNanddeep Nachan
The document discusses the Microsoft Power Platform Center of Excellence (CoE) Starter Kit. It provides an overview of the CoE Starter Kit and its modules, including core components, governance components, nurture components, and theming components. It describes how to set up the CoE Starter Kit and its modules as well as some limitations. References for more information on the CoE, CoE Starter Kit, and core components are also provided.
In this presentation, Raghavendra BM of Valuebound has discussed the basics of MongoDB - an open-source document database and leading NoSQL database.
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This document contains a presentation by Abhijeet Anand on NumPy. It introduces NumPy as a Python library for working with arrays, which aims to provide array objects that are faster than traditional Python lists. NumPy arrays benefit from being stored continuously in memory, unlike lists. The presentation covers 1D, 2D and 3D arrays in NumPy and basic array properties and operations like shape, size, dtype, copying, sorting, addition, subtraction and more.
Apache Iceberg: An Architectural Look Under the CoversScyllaDB
Data Lakes have been built with a desire to democratize data - to allow more and more people, tools, and applications to make use of data. A key capability needed to achieve it is hiding the complexity of underlying data structures and physical data storage from users. The de-facto standard has been the Hive table format addresses some of these problems but falls short at data, user, and application scale. So what is the answer? Apache Iceberg.
Apache Iceberg table format is now in use and contributed to by many leading tech companies like Netflix, Apple, Airbnb, LinkedIn, Dremio, Expedia, and AWS.
Watch Alex Merced, Developer Advocate at Dremio, as he describes the open architecture and performance-oriented capabilities of Apache Iceberg.
You will learn:
• The issues that arise when using the Hive table format at scale, and why we need a new table format
• How a straightforward, elegant change in table format structure has enormous positive effects
• The underlying architecture of an Apache Iceberg table, how a query against an Iceberg table works, and how the table’s underlying structure changes as CRUD operations are done on it
• The resulting benefits of this architectural design
Organizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.
The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...Databricks
Many had dubbed 2020 as the decade of data. This is indeed an era of data zeitgeist.
From code-centric software development 1.0, we are entering software development 2.0, a data-centric and data-driven approach, where data plays a central theme in our everyday lives.
As the volume and variety of data garnered from myriad data sources continue to grow at an astronomical scale and as cloud computing offers cheap computing and data storage resources at scale, the data platforms have to match in their abilities to process, analyze, and visualize at scale and speed and with ease — this involves data paradigm shifts in processing and storing and in providing programming frameworks to developers to access and work with these data platforms.
In this talk, we will survey some emerging technologies that address the challenges of data at scale, how these tools help data scientists and machine learning developers with their data tasks, why they scale, and how they facilitate the future data scientists to start quickly.
In particular, we will examine in detail two open-source tools MLflow (for machine learning life cycle development) and Delta Lake (for reliable storage for structured and unstructured data).
Other emerging tools such as Koalas help data scientists to do exploratory data analysis at scale in a language and framework they are familiar with as well as emerging data + AI trends in 2021.
You will understand the challenges of machine learning model development at scale, why you need reliable and scalable storage, and what other open source tools are at your disposal to do data science and machine learning at scale.
As organizations pursue Big Data initiatives to capture new opportunities for data-driven insights, data governance has become table stakes both from the perspective of external regulatory compliance as well as business value extraction internally within an enterprise. This session will introduce Apache Atlas, a project that was incubated by Hortonworks along with a group of industry leaders across several verticals including financial services, healthcare, pharma, oil and gas, retail and insurance to help address data governance and metadata needs with an open extensible platform governed under the aegis of Apache Software Foundation. Apache Atlas empowers organizations to harvest metadata across the data ecosystem, govern and curate data lakes by applying consistent data classification with a centralized metadata catalog.
In this talk, we will present the underpinnings of the architecture of Apache Atlas and conclude with a tour of governance capabilities within Apache Atlas as we showcase various features for open metadata modeling, data classification, visualizing cross-component lineage and impact. We will also demo how Apache Atlas delivers a complete view of data movement across several analytic engines such as Apache Hive, Apache Storm, Apache Kafka and capabilities to effectively classify, discover datasets.
Apache Atlas provides centralized metadata services and cross-component dataset lineage tracking for Hadoop components. It aims to enable transparent, reproducible, auditable and consistent data governance across structured, unstructured, and traditional database systems. The near term roadmap includes dynamic access policy driven by metadata and enhanced Hive integration. Apache Atlas also pursues metadata exchange with non-Hadoop systems and third party vendors through REST APIs and custom reporters.
Streaming Real-time Data to Azure Data Lake Storage Gen 2Carole Gunst
Check out this presentation to learn the basics of using Attunity Replicate to stream real-time data to Azure Data Lake Storage Gen2 for analytics projects.
Every day, businesses across a wide variety of industries share data to support insights that drive efficiency and new business opportunities. However, existing methods for sharing data involve great effort on the part of data providers to share data, and involve great effort on the part of data customers to make use of that data.
However, existing approaches to data sharing (such as e-mail, FTP, EDI, and APIs) have significant overhead and friction. For one, legacy approaches such as e-mail and FTP were never intended to support the big data volumes of today. Other data sharing methods also involve enormous effort. All of these methods require not only that the data be extracted, copied, transformed, and loaded, but also that related schemas and metadata must be transported as well. This creates a burden on data providers to deconstruct and stage data sets. This burden and effort is mirrored for the data recipient, who must reconstruct the data.
As a result, companies are handicapped in their ability to fully realize the value in their data assets.
Snowflake Data Sharing allows companies to grant instant access to ready-to-use data to any number of partners or data customers without any data movement, copying, or complex pipelines.
Using Snowflake Data Sharing, companies can derive new insights and value from data much more quickly and with significantly less effort than current data sharing methods. As a result, companies now have a new approach and a powerful new tool to get the full value out of their data assets.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
Building End-to-End Delta Pipelines on GCPDatabricks
Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years.
Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines.
In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.
This document outlines an agenda for a 90-minute workshop on Snowflake. The agenda includes introductions, an overview of Snowflake and data warehousing, demonstrations of how users utilize Snowflake, hands-on exercises loading sample data and running queries, and discussions of Snowflake architecture and capabilities. Real-world customer examples are also presented, such as a pharmacy building new applications on Snowflake and an education company using it to unify their data sources and achieve a 16x performance improvement.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
At wetter.com we build analytical B2B data products and heavily use Spark and AWS technologies for data processing and analytics. I explain why we moved from AWS EMR to Databricks and Delta and share our experiences from different angles like architecture, application logic and user experience. We will look how security, cluster configuration, resource consumption and workflow changed by using Databricks clusters as well as how using Delta tables simplified our application logic and data operations.
Putting the Ops in DataOps: Orchestrate the Flow of Data Across Data PipelinesDATAVERSITY
With the aid of any number of data management and processing tools, data flows through multiple on-prem and cloud storage locations before it’s delivered to business users. As a result, IT teams — including IT Ops, DataOps, and DevOps — are often overwhelmed by the complexity of creating a reliable data pipeline that includes the automation and observability they require.
The answer to this widespread problem is a centralized data pipeline orchestration solution.
Join Stonebranch’s Scott Davis, Global Vice President and Ravi Murugesan, Sr. Solution Engineer to learn how DataOps teams orchestrate their end-to-end data pipelines with a platform approach to managing automation.
Key Learnings:
- Discover how to orchestrate data pipelines across a hybrid IT environment (on-prem and cloud)
- Find out how DataOps teams are empowered with event-based triggers for real-time data flow
- See examples of reports, dashboards, and proactive alerts designed to help you reliably keep data flowing through your business — with the observability you require
- Discover how to replace clunky legacy approaches to streaming data in a multi-cloud environment
- See what’s possible with the Stonebranch Universal Automation Center (UAC)
Unified MLOps: Feature Stores & Model DeploymentDatabricks
If you’ve brought two or more ML models into production, you know the struggle that comes from managing multiple data sets, feature engineering pipelines, and models. This talk will propose a whole new approach to MLOps that allows you to successfully scale your models, without increasing latency, by merging a database, a feature store, and machine learning.
Splice Machine is a hybrid (HTAP) database built upon HBase and Spark. The database powers a one of a kind single-engine feature store, as well as the deployment of ML models as tables inside the database. A simple JDBC connection means Splice Machine can be used with any model ops environment, such as Databricks.
The HBase side allows us to serve features to deployed ML models, and generate ML predictions, in milliseconds. Our unique Spark engine allows us to generate complex training sets, as well as ML predictions on petabytes of data.
In this talk, Monte will discuss how his experience running the AI lab at NASA, and as CEO of Red Pepper, Blue Martini Software and Rocket Fuel, led him to create Splice Machine. Jack will give a quick demonstration of how it all works.
Apache Kafka, Tiered Storage and TensorFlow for Streaming Machine Learning wi...Kai Wähner
Don’t underestimate the Hidden Technical Debt in Machine Learning Systems.
Leverage Apache Kafka’s open ecosystem as a scalable and flexible Event Streaming Platform to build one pipeline for real-time and batch use cases.
Use Streaming Machine Learning with Apache Kafka, Tiered Storage, and TensorFlow IO to simplify your big data architecture.
Tiered Storage for Kafka provides:
- one platform for all data processing
- an event-based source of truth for materialized views
- no need for a pipeline between Kafka and a Data Lake like Hadoop
Benefits:
- cost reduction
- long-term backup
- performance isolation (real-time and historical analysis in the same cluster)
Use Cases for Reprocessing Historical Events:
- New consumer application
- Error-handling
- Compliance / regulatory processing
- Query and analyze existing events
- Model training
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
Delta Lake brings reliability, performance, and security to data lakes. It provides ACID transactions, schema enforcement, and unified handling of batch and streaming data to make data lakes more reliable. Delta Lake also features lightning fast query performance through its optimized Delta Engine. It enables security and compliance at scale through access controls and versioning of data. Delta Lake further offers an open approach and avoids vendor lock-in by using open formats like Parquet that can integrate with various ecosystems.
The Modern Data Team for the Modern Data Stack: dbt and the Role of the Analy...Databricks
A traditional data team has roles including data engineer, data scientist, and data analyst. However, many organizations are finding success by integrating a new role – the analytics engineer. The analytics engineer develops a code-based data infrastructure that can serve both analytics and data science teams. He or she develops re-usable data models using the software engineering practices of version control and unit testing, and provides the critical domain expertise that ensures that data products are relevant and insightful. In this talk we’ll talk about the role and skill set of the analytics engineer, and discuss how dbt, an open source programming environment, empowers anyone with a SQL skillset to fulfill this new role on the data team. We’ll demonstrate how to use dbt to build version-controlled data models on top of Delta Lake, test both the code and our assumptions about the underlying data, and orchestrate complete data pipelines on Apache Spark™.
A deep dive into running data analytic workloads in the cloudCloudera, Inc.
This document discusses running data analytic workloads in the cloud using Cloudera Altus. It introduces Altus, which provides a platform-as-a-service for analyzing and processing data at scale in public clouds. The document outlines Altus features like low cost per-hour pricing, end-user focus, and cloud-native deployment. It then describes hands-on examples using Altus Data Engineering for ETL and the Altus Analytic Database for exploration and analytics. Workload analytics capabilities are also introduced for troubleshooting and optimizing jobs.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Organizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Databricks CEO Ali Ghodsi introduces Databricks Delta, a new data management system that combines the scale and cost-efficiency of a data lake, the performance and reliability of a data warehouse, and the low latency of streaming.
The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...Databricks
Many had dubbed 2020 as the decade of data. This is indeed an era of data zeitgeist.
From code-centric software development 1.0, we are entering software development 2.0, a data-centric and data-driven approach, where data plays a central theme in our everyday lives.
As the volume and variety of data garnered from myriad data sources continue to grow at an astronomical scale and as cloud computing offers cheap computing and data storage resources at scale, the data platforms have to match in their abilities to process, analyze, and visualize at scale and speed and with ease — this involves data paradigm shifts in processing and storing and in providing programming frameworks to developers to access and work with these data platforms.
In this talk, we will survey some emerging technologies that address the challenges of data at scale, how these tools help data scientists and machine learning developers with their data tasks, why they scale, and how they facilitate the future data scientists to start quickly.
In particular, we will examine in detail two open-source tools MLflow (for machine learning life cycle development) and Delta Lake (for reliable storage for structured and unstructured data).
Other emerging tools such as Koalas help data scientists to do exploratory data analysis at scale in a language and framework they are familiar with as well as emerging data + AI trends in 2021.
You will understand the challenges of machine learning model development at scale, why you need reliable and scalable storage, and what other open source tools are at your disposal to do data science and machine learning at scale.
As organizations pursue Big Data initiatives to capture new opportunities for data-driven insights, data governance has become table stakes both from the perspective of external regulatory compliance as well as business value extraction internally within an enterprise. This session will introduce Apache Atlas, a project that was incubated by Hortonworks along with a group of industry leaders across several verticals including financial services, healthcare, pharma, oil and gas, retail and insurance to help address data governance and metadata needs with an open extensible platform governed under the aegis of Apache Software Foundation. Apache Atlas empowers organizations to harvest metadata across the data ecosystem, govern and curate data lakes by applying consistent data classification with a centralized metadata catalog.
In this talk, we will present the underpinnings of the architecture of Apache Atlas and conclude with a tour of governance capabilities within Apache Atlas as we showcase various features for open metadata modeling, data classification, visualizing cross-component lineage and impact. We will also demo how Apache Atlas delivers a complete view of data movement across several analytic engines such as Apache Hive, Apache Storm, Apache Kafka and capabilities to effectively classify, discover datasets.
Apache Atlas provides centralized metadata services and cross-component dataset lineage tracking for Hadoop components. It aims to enable transparent, reproducible, auditable and consistent data governance across structured, unstructured, and traditional database systems. The near term roadmap includes dynamic access policy driven by metadata and enhanced Hive integration. Apache Atlas also pursues metadata exchange with non-Hadoop systems and third party vendors through REST APIs and custom reporters.
Streaming Real-time Data to Azure Data Lake Storage Gen 2Carole Gunst
Check out this presentation to learn the basics of using Attunity Replicate to stream real-time data to Azure Data Lake Storage Gen2 for analytics projects.
Every day, businesses across a wide variety of industries share data to support insights that drive efficiency and new business opportunities. However, existing methods for sharing data involve great effort on the part of data providers to share data, and involve great effort on the part of data customers to make use of that data.
However, existing approaches to data sharing (such as e-mail, FTP, EDI, and APIs) have significant overhead and friction. For one, legacy approaches such as e-mail and FTP were never intended to support the big data volumes of today. Other data sharing methods also involve enormous effort. All of these methods require not only that the data be extracted, copied, transformed, and loaded, but also that related schemas and metadata must be transported as well. This creates a burden on data providers to deconstruct and stage data sets. This burden and effort is mirrored for the data recipient, who must reconstruct the data.
As a result, companies are handicapped in their ability to fully realize the value in their data assets.
Snowflake Data Sharing allows companies to grant instant access to ready-to-use data to any number of partners or data customers without any data movement, copying, or complex pipelines.
Using Snowflake Data Sharing, companies can derive new insights and value from data much more quickly and with significantly less effort than current data sharing methods. As a result, companies now have a new approach and a powerful new tool to get the full value out of their data assets.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
Building End-to-End Delta Pipelines on GCPDatabricks
Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years.
Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines.
In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.
This document outlines an agenda for a 90-minute workshop on Snowflake. The agenda includes introductions, an overview of Snowflake and data warehousing, demonstrations of how users utilize Snowflake, hands-on exercises loading sample data and running queries, and discussions of Snowflake architecture and capabilities. Real-world customer examples are also presented, such as a pharmacy building new applications on Snowflake and an education company using it to unify their data sources and achieve a 16x performance improvement.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
At wetter.com we build analytical B2B data products and heavily use Spark and AWS technologies for data processing and analytics. I explain why we moved from AWS EMR to Databricks and Delta and share our experiences from different angles like architecture, application logic and user experience. We will look how security, cluster configuration, resource consumption and workflow changed by using Databricks clusters as well as how using Delta tables simplified our application logic and data operations.
Putting the Ops in DataOps: Orchestrate the Flow of Data Across Data PipelinesDATAVERSITY
With the aid of any number of data management and processing tools, data flows through multiple on-prem and cloud storage locations before it’s delivered to business users. As a result, IT teams — including IT Ops, DataOps, and DevOps — are often overwhelmed by the complexity of creating a reliable data pipeline that includes the automation and observability they require.
The answer to this widespread problem is a centralized data pipeline orchestration solution.
Join Stonebranch’s Scott Davis, Global Vice President and Ravi Murugesan, Sr. Solution Engineer to learn how DataOps teams orchestrate their end-to-end data pipelines with a platform approach to managing automation.
Key Learnings:
- Discover how to orchestrate data pipelines across a hybrid IT environment (on-prem and cloud)
- Find out how DataOps teams are empowered with event-based triggers for real-time data flow
- See examples of reports, dashboards, and proactive alerts designed to help you reliably keep data flowing through your business — with the observability you require
- Discover how to replace clunky legacy approaches to streaming data in a multi-cloud environment
- See what’s possible with the Stonebranch Universal Automation Center (UAC)
Unified MLOps: Feature Stores & Model DeploymentDatabricks
If you’ve brought two or more ML models into production, you know the struggle that comes from managing multiple data sets, feature engineering pipelines, and models. This talk will propose a whole new approach to MLOps that allows you to successfully scale your models, without increasing latency, by merging a database, a feature store, and machine learning.
Splice Machine is a hybrid (HTAP) database built upon HBase and Spark. The database powers a one of a kind single-engine feature store, as well as the deployment of ML models as tables inside the database. A simple JDBC connection means Splice Machine can be used with any model ops environment, such as Databricks.
The HBase side allows us to serve features to deployed ML models, and generate ML predictions, in milliseconds. Our unique Spark engine allows us to generate complex training sets, as well as ML predictions on petabytes of data.
In this talk, Monte will discuss how his experience running the AI lab at NASA, and as CEO of Red Pepper, Blue Martini Software and Rocket Fuel, led him to create Splice Machine. Jack will give a quick demonstration of how it all works.
Apache Kafka, Tiered Storage and TensorFlow for Streaming Machine Learning wi...Kai Wähner
Don’t underestimate the Hidden Technical Debt in Machine Learning Systems.
Leverage Apache Kafka’s open ecosystem as a scalable and flexible Event Streaming Platform to build one pipeline for real-time and batch use cases.
Use Streaming Machine Learning with Apache Kafka, Tiered Storage, and TensorFlow IO to simplify your big data architecture.
Tiered Storage for Kafka provides:
- one platform for all data processing
- an event-based source of truth for materialized views
- no need for a pipeline between Kafka and a Data Lake like Hadoop
Benefits:
- cost reduction
- long-term backup
- performance isolation (real-time and historical analysis in the same cluster)
Use Cases for Reprocessing Historical Events:
- New consumer application
- Error-handling
- Compliance / regulatory processing
- Query and analyze existing events
- Model training
[DSC Europe 22] Lakehouse architecture with Delta Lake and Databricks - Draga...DataScienceConferenc1
Dragan Berić will take a deep dive into Lakehouse architecture, a game-changing concept bridging the best elements of data lake and data warehouse. The presentation will focus on the Delta Lake format as the foundation of the Lakehouse philosophy, and Databricks as the primary platform for its implementation.
Delta Lake brings reliability, performance, and security to data lakes. It provides ACID transactions, schema enforcement, and unified handling of batch and streaming data to make data lakes more reliable. Delta Lake also features lightning fast query performance through its optimized Delta Engine. It enables security and compliance at scale through access controls and versioning of data. Delta Lake further offers an open approach and avoids vendor lock-in by using open formats like Parquet that can integrate with various ecosystems.
The Modern Data Team for the Modern Data Stack: dbt and the Role of the Analy...Databricks
A traditional data team has roles including data engineer, data scientist, and data analyst. However, many organizations are finding success by integrating a new role – the analytics engineer. The analytics engineer develops a code-based data infrastructure that can serve both analytics and data science teams. He or she develops re-usable data models using the software engineering practices of version control and unit testing, and provides the critical domain expertise that ensures that data products are relevant and insightful. In this talk we’ll talk about the role and skill set of the analytics engineer, and discuss how dbt, an open source programming environment, empowers anyone with a SQL skillset to fulfill this new role on the data team. We’ll demonstrate how to use dbt to build version-controlled data models on top of Delta Lake, test both the code and our assumptions about the underlying data, and orchestrate complete data pipelines on Apache Spark™.
A deep dive into running data analytic workloads in the cloudCloudera, Inc.
This document discusses running data analytic workloads in the cloud using Cloudera Altus. It introduces Altus, which provides a platform-as-a-service for analyzing and processing data at scale in public clouds. The document outlines Altus features like low cost per-hour pricing, end-user focus, and cloud-native deployment. It then describes hands-on examples using Altus Data Engineering for ETL and the Altus Analytic Database for exploration and analytics. Workload analytics capabilities are also introduced for troubleshooting and optimizing jobs.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Leveraging the Cloud for Big Data Analytics 12.11.18Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on AWS. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
In this webinar, we’ll show you how Cloudera SDX reduces the complexity in your data management environment and lets you deliver diverse analytics with consistent security, governance, and lifecycle management against a shared data catalog.
Cloudera Altus: Big Data in the Cloud Made EasyCloudera, Inc.
Cloudera Altus makes it easier for data engineers, ETL developers, and anyone who regularly works with raw data to process that data in the cloud efficiently and cost effectively. In this webinar we introduce our new platform-as-a-service offering and explore challenges associated with data processing in the cloud today, how Altus abstracts cluster overhead to deliver easy, efficient data processing, and unique features and benefits of Cloudera Altus.
Leveraging the cloud for analytics and machine learning 1.29.19Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on Azure. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Cloudera Altus: Big Data in der Cloud einfach gemachtCloudera, Inc.
Neueste Studien zeigen, dass Data Scientisten und Analysten bis zu 80% ihrer Zeit dafür nutzen, Daten zu reinigen und vorzubereiten.
Eine ohnehin schon zeitaufwändige Aufgabe kann in der Cloud noch weiter erschwert werden, da das Cluster Management und Operations die Komplexität noch erhöhen.
Nutzer wünschen sich daher, diese komplexen Workflows zu vereinheitlichen und zu vereinfachen.
Um Big Data und Machine Learning Initiativen voranzutreiben, benötigen Unternehmen eine skalierbare und überall verfügbare Plattform. Diese muss Self-Service ermöglichen und Datensilos eliminieren.
The 6th Wave of Automation: Automation of Decisions | Cloudera Analytics & Ma...Cloudera, Inc.
This presentation provides detail on how we are now in the 6th wave of automation, that is based on Machine Learning. In this 6th wave, Cloudera plays a critical role in providing the data platform for Machine Learning and Analytics built for the Cloud.
How to Build Multi-disciplinary Analytics Applications on a Shared Data PlatformCloudera, Inc.
The document discusses building multi-disciplinary analytics applications on a shared data platform. It describes challenges with traditional fragmented approaches using multiple data silos and tools. A shared data platform with Cloudera SDX provides a common data experience across workloads through shared metadata, security, and governance services. This approach optimizes key design goals and provides business benefits like increased insights, agility, and decreased costs compared to siloed environments. An example application of predictive maintenance is given to improve fleet performance.
Cloud-Native Machine Learning: Emerging Trends and the Road AheadDataWorks Summit
Big data platforms are being asked to support an ever increasing range of workloads and compute environments, including large-scale machine learning and public and private clouds. In this talk, we will discuss some emerging capabilities around cloud-native machine learning and data engineering, including running machine learning and Spark workloads directly on Kubernetes, and share our vision of the road ahead for ML and AI in the cloud.
Webinar | From Zero to 1 Million with Google Cloud Platform and DataStaxDataStax
Google Cloud Platform delivers the industry’s leading cloud-based services to create anything from simple websites to complex applications. DataStax delivers Apache Cassandra™, the leading distributed database technology, to the enterprise. Together, DataStax Enterprise on Google Cloud Platform delivers the performance, agility, infinite elasticity and innovation organizations need to build high-performance, highly-available online applications.
Join Allan Naim, Global Product Lead at Google Cloud Platform and Darshan Rawal, Sr. Director of Product Management at DataStax as they share their expertise on why DataStax and Google Cloud Platform deliver the industry’s most robust Infrastructure-as-a Service (IaaS) platform and how your organization find success with NoSQL and Cloud services.
View to learn how to:
- Handle more than 1 Million requests per second for data-intensive online applications with Apache Cassandra on Google Cloud Platform
- Leverage the technology infrastructure and global network powering Google’s search engine with DataStax to deploy blazing-fast and always-on applications
- Transform your business into a data-driven company, a change that is critical as future success and discoveries hinge on the ability to quickly take action on data
Multidisziplinäre Analyseanwendungen auf einer gemeinsamen Datenplattform ers...Cloudera, Inc.
Maschinelles Lernen und Analyseanwendungen explodieren im Unternehmen und ermöglichen Anwendungsfällen in Bereichen wie vorbeugende Wartung, Bereitstellung neuer, wünschenswerter Produktangebote für Kunden zum richtigen Zeitpunkt und Bekämpfung von Insider-Bedrohungen für Ihr Unternehmen.
It’s becoming clear that enterprises need more than one cloud. Hybrid enables enterprises to optimize how their business works – public cloud for elasticity and scale, multi-cloud for redundancy and choice, and on-premises for performance and privacy. Cloudera delivers a hybrid cloud solution that works where enterprises work, with the agility, security and governance enterprise IT needs, and the self-service analytics business people and enterprise data professionals demand. In this session, we will talk about how Cloudera helps deliver hybrid solutions for enterprises and will run a hands-on Cloudera PaaS demo to exhibit:
- Altus Environment Setup
- Configure Altus SDX
- Spin-up transient clusters with Altus
- Execute workload on Altus Data Engineering clusters
- Run interactive queries on object store with Altus Data Warehouse
- Job Analytics with Workload Experience Manager (WXM)
Speaker: Junaid Rao, Senior Cloud Sales Engineer, Cloudera
Build a modern platform for anti-money laundering 9.19.18Cloudera, Inc.
In this webinar, you will learn how Cloudera and BAH riskCanvas can help you build a modern AML platform that reduces false positive rates, investigation costs, technology sprawl, and regulatory risk.
Comment développer une stratégie Big Data dans le cloud public avec l'offre P...Cloudera, Inc.
Le cloud public est une proposition attractive pour les entreprises à la recherche d’agilité dans leurs projets big data, qu’il s’agisse de traiter des données en masse ou d’y exécuter des analyses complexes pour une meilleure prise de décision.
Cloudera Analytics and Machine Learning Platform - Optimized for Cloud Stefan Lipp
Take Data Management to the next level: Connect Analytics and Machine Learning in a single governed platform consisting of a curated protable open source stack. Run this platform on-prem, hybrid or multicloud, reuse code and models avoid lock-in.
Turning Data into Business Value with a Modern Data PlatformCloudera, Inc.
The document discusses how data has become a strategic asset for businesses and how a modern data platform can help organizations drive customer insights, improve products and services, lower business risks, and modernize IT. It provides examples of companies using analytics to personalize customer solutions, detect sepsis early to save lives, and protect the global finance system. The document also outlines the evolution of Hadoop platforms and how Cloudera Enterprise provides a common workload pattern to store, process, and analyze data across different workloads and databases in a fast, easy, and secure manner.
Big data journey to the cloud 5.30.18 asher bartchCloudera, Inc.
We hope this session was valuable in teaching you more about Cloudera Enterprise on AWS, and how fast and easy it is to deploy a modern data management platform—in your cloud and on your terms.
Optimize your cloud strategy for machine learning and analyticsCloudera, Inc.
Join industry superstars Mike Olson (Cloudera CSO and co-founder) and Jim Curtis (451 Research senior analyst) as they outline the best practices for cloud-based machine learning and analytics in this “can’t miss” webinar.
Hot topics include:
Why enterprises are moving their analytics to the public cloud
How to select the best cloud deployment model
Design tricks that make cloud economics work
Success stories, cautionary tales, and lessons learned
James will share 451 Research findings and offer insights learned from surveying both the vendor landscape and enterprise practitioners.
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Mike will regale you with his vision for the future of multi-disciplinary machine learning and analytics in hybrid- and multi-cloud environments
3 things to learn:
Why enterprises are moving their analytics to the public cloud
How to select the best cloud deployment model
Design tricks that make cloud economics work
The document discusses using Cloudera DataFlow to address challenges with collecting, processing, and analyzing log data across many systems and devices. It provides an example use case of logging modernization to reduce costs and enable security solutions by filtering noise from logs. The presentation shows how DataFlow can extract relevant events from large volumes of raw log data and normalize the data to make security threats and anomalies easier to detect across many machines.
Cloudera Data Impact Awards 2021 - Finalists Cloudera, Inc.
The document outlines the 2021 finalists for the annual Data Impact Awards program, which recognizes organizations using Cloudera's platform and the impactful applications they have developed. It provides details on the challenges, solutions, and outcomes for each finalist project in the categories of Data Lifecycle Connection, Cloud Innovation, Data for Enterprise AI, Security & Governance Leadership, Industry Transformation, People First, and Data for Good. There are multiple finalists highlighted in each category demonstrating innovative uses of data and analytics.
2020 Cloudera Data Impact Awards FinalistsCloudera, Inc.
Cloudera is proud to present the 2020 Data Impact Awards Finalists. This annual program recognizes organizations running the Cloudera platform for the applications they've built and the impact their data projects have on their organizations, their industries, and the world. Nominations were evaluated by a panel of independent thought-leaders and expert industry analysts, who then selected the finalists and winners. Winners exemplify the most-cutting edge data projects and represent innovation and leadership in their respective industries.
The document outlines the agenda for Cloudera's Enterprise Data Cloud event in Vienna. It includes welcome remarks, keynotes on Cloudera's vision and customer success stories. There will be presentations on the new Cloudera Data Platform and customer case studies, followed by closing remarks. The schedule includes sessions on Cloudera's approach to data warehousing, machine learning, streaming and multi-cloud capabilities.
Machine Learning with Limited Labeled Data 4/3/19Cloudera, Inc.
Cloudera Fast Forward Labs’ latest research report and prototype explore learning with limited labeled data. This capability relaxes the stringent labeled data requirement in supervised machine learning and opens up new product possibilities. It is industry invariant, addresses the labeling pain point and enables applications to be built faster and more efficiently.
Data Driven With the Cloudera Modern Data Warehouse 3.19.19Cloudera, Inc.
In this session, we will cover how to move beyond structured, curated reports based on known questions on known data, to an ad-hoc exploration of all data to optimize business processes and into the unknown questions on unknown data, where machine learning and statistically motivated predictive analytics are shaping business strategy.
Introducing Cloudera DataFlow (CDF) 2.13.19Cloudera, Inc.
Watch this webinar to understand how Hortonworks DataFlow (HDF) has evolved into the new Cloudera DataFlow (CDF). Learn about key capabilities that CDF delivers such as -
-Powerful data ingestion powered by Apache NiFi
-Edge data collection by Apache MiNiFi
-IoT-scale streaming data processing with Apache Kafka
-Enterprise services to offer unified security and governance from edge-to-enterprise
Introducing Cloudera Data Science Workbench for HDP 2.12.19Cloudera, Inc.
Cloudera’s Data Science Workbench (CDSW) is available for Hortonworks Data Platform (HDP) clusters for secure, collaborative data science at scale. During this webinar, we provide an introductory tour of CDSW and a demonstration of a machine learning workflow using CDSW on HDP.
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19Cloudera, Inc.
Join Cloudera as we outline how we use Cloudera technology to strengthen sales engagement, minimize marketing waste, and empower line of business leaders to drive successful outcomes.
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19Cloudera, Inc.
Join us to learn about the challenges of legacy data warehousing, the goals of modern data warehousing, and the design patterns and frameworks that help to accelerate modernization efforts.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
The document discusses the benefits and trends of modernizing a data warehouse. It outlines how a modern data warehouse can provide deeper business insights at extreme speed and scale while controlling resources and costs. Examples are provided of companies that have improved fraud detection, customer retention, and machine performance by implementing a modern data warehouse that can handle large volumes and varieties of data from many sources.
Extending Cloudera SDX beyond the PlatformCloudera, Inc.
Cloudera SDX is by no means no restricted to just the platform; it extends well beyond. In this webinar, we show you how Bardess Group’s Zero2Hero solution leverages the shared data experience to coordinate Cloudera, Trifacta, and Qlik to deliver complete customer insight.
Federated Learning: ML with Privacy on the Edge 11.15.18Cloudera, Inc.
Join Cloudera Fast Forward Labs Research Engineer, Mike Lee Williams, to hear about their latest research report and prototype on Federated Learning. Learn more about what it is, when it’s applicable, how it works, and the current landscape of tools and libraries.
Analyst Webinar: Doing a 180 on Customer 360Cloudera, Inc.
451 Research Analyst Sheryl Kingstone, and Cloudera’s Steve Totman recently discussed how a growing number of organizations are replacing legacy Customer 360 systems with Customer Insights Platforms.
Introducing the data science sandbox as a service 8.30.18Cloudera, Inc.
How can companies integrate data science into their businesses more effectively? Watch this recorded webinar and demonstration to hear more about operationalizing data science with Cloudera Data Science Workbench on Cazena’s fully-managed cloud platform.
Workload Experience Manager (XM) gives you the visibility necessary to efficiently migrate, analyze, optimize, and scale workloads running in a modern data warehouse. In this recorded webinar we discuss common challenges running at scale with modern data warehouse, benefits of end-to-end visibility into workload lifecycles, overview of Workload XM and live demo, real-life customer before/after scenarios, and what's next for Workload XM.
Get started with Cloudera's cyber solutionCloudera, Inc.
Cloudera empowers cybersecurity innovators to proactively secure the enterprise by accelerating threat detection, investigation, and response through machine learning and complete enterprise visibility. Cloudera’s cybersecurity solution, based on Apache Spot, enables anomaly detection, behavior analytics, and comprehensive access across all enterprise data using an open, scalable platform. But what’s the easiest way to get started?
Spark and Deep Learning Frameworks at Scale 7.19.18Cloudera, Inc.
We'll outline approaches for preprocessing, training, inference, and deployment across datasets (time series, audio, video, text, etc.) that leverage Spark, along with its extended ecosystem of libraries and deep learning frameworks using Cloudera's Data Science Workbench.
Cloud Data Warehousing with Cloudera Altus 7.24.18Cloudera, Inc.
This webinar will help you maximize the full potential of the cloud. Understand how to leverage cloud environments for different analytic workloads to empower business analysts and keep IT happy. An intricate, beautiful balance. The learn best practices in design, performance tuning, workload considerations, and hybrid or multi-cloud strategies.
This comprehensive Data Science course is designed to equip learners with the essential skills and knowledge required to analyze, interpret, and visualize complex data. Covering both theoretical concepts and practical applications, the course introduces tools and techniques used in the data science field, such as Python programming, data wrangling, statistical analysis, machine learning, and data visualization.
Mieke Jans is a Manager at Deloitte Analytics Belgium. She learned about process mining from her PhD supervisor while she was collaborating with a large SAP-using company for her dissertation.
Mieke extended her research topic to investigate the data availability of process mining data in SAP and the new analysis possibilities that emerge from it. It took her 8-9 months to find the right data and prepare it for her process mining analysis. She needed insights from both process owners and IT experts. For example, one person knew exactly how the procurement process took place at the front end of SAP, and another person helped her with the structure of the SAP-tables. She then combined the knowledge of these different persons.
Just-in-time: Repetitive production system in which processing and movement of materials and goods occur just as they are needed, usually in small batches
JIT is characteristic of lean production systems
JIT operates with very little “fat”
#3: We began with a simple premise courtesy of Simon Sinek and his wildly popular Golden Circle framework.
https://www.ted.com/talks/simon_sinek_how_great_leaders_inspire_action
Great companies start with “why” versus with what. Sounds simple right? But it isn’t. It requires deep introspection and honesty. In our case, we came to the conclusion that what we really believe is that data has the power to make what is impossible today, possible tomorrow. And that isn’t simply a slogan or a tagline. We believe it in our core and we are making this belief more real everyday. In fact, many of you are probably wondering what the graphic is on the right. Well it comes to us courtesy of our friends at NASA, who are using Cloudera technology to help make the first man-mission to Mars a really. They plan to collect, analyze and act upon “petabytes of data on a daily basis” to ensure the safe delivery and return of the brave astronauts taking on this bold mission. So as you can see, we really mean it when we say making what’s impossible today, possible tomorrow.
#4: Ok, now that you understand why we do what we do at Cloudera and what drives us each and every day, you probably want to better understand “How” we do it. Well simply stated, we help people, like all of you in attendance here today” to transform the endless amount of data you are undoubtedly collecting into clear and actionable insights. In other words, we help turn data into action. We are automating the decision making process. Specifically, we do this in 3 ways.
Protect your business. At the most base level, sort of the lowest level of the organizational Maslow’s hierarchy, is protecting your business, your customers and your employees. As we have all seen, this is becoming increasingly difficult as a wide range of new threats arise each and every day…such as the recent WannaCry worm clearly illustrated. At Cloudera, we take the security of your data, all of your data, very seriously and we’ve built specific solutions to ensure that it remains well protected.
Connect products and services. The next thing we empower people to do is to connect all of the data generated by their products and services and transform it into actionable insights. This is critical for an increasingly connected world – an Internet of Things, or IoT, world – where everything and everybody is connected. So whether you are connecting vehicle sensors, kitchen appliances, or health care monitors – Cloudera can help.
Drive customer insights. To continue with the Maslow’s hierarchy metaphor, one of the toughest things for any organization to do is to grow – quickly and predictably – it is the equivalent of self-actualization for an individual. After all, cutting costs is actually pretty easy, perhaps not fun, but easy to execute. Growing a business on the other hand will require that you make full use of your most valuable resource – your data. You must collect and analyze all customer and prospective customer data if you want a leg up on the competition. Cloudera helps hundreds of customers grow their businesses by analyzing, predicting and acting on insights gleaned from their customer data.
#5: OK. Now that you know why we do what we do – imagining new possibilities through the power of data. And, you know how we do it – by helping businesses become more secure, better connected and higher growing. I’m sure you’re interested in what we do. Well, simply stated, we deliver the modern data platform for machine learning and advanced analytics. The core technologies for collecting, storing and analyzing data that were built decades ago, simply won’t deliver the speed, scale, agility and security needed for a world suddenly awash in massive quantities of new, important data. Companies like Google and Yahoo discovered this first, and the race to Big Data was born. Our founders at Cloudera were among the first to see this opportunity. Today, we are proud that what was once a dream is now a reality. Today, Cloudera provides a modern platform that literally runs anywhere – on your premises, in the public cloud, or any combination you can imagine. That type of flexibility enables our customers to run in the most agile and cost-efficient environment they need for their unique business needs. It also provides them with the enterprise-grade capabilities they need to run a secure, high-performance and well governed data infrastructure. Perhaps most importantly, this sound foundation provides the ultimate platform for the advanced machine learning and analytic workloads that are changing the way we work – enabling everything from predictive maintenance to predictive medicine and beyond.
#8: Cloudera delivers an integrated suite of capabilities for data management, machine learning and advanced analytics, affording customers an agile, scalable and cost effective solution for transforming their businesses.
Cloudera unites the best of both worlds for massive enterprise scale
Data Science & Data Engineering
Advanced Analytics
Operational Processing
#9: The SCP Support Standard provides clear guidelines that enable organizations to:
Increase customer satisfaction and loyalty by improving operational effectiveness and staff productivity
Implement a continuous improvement program to achieve and maintain world-class levels of performance
Benchmark technical support operations against best in class organizations and best practices to further enhance performance
Leveraging SCP Standards helps to improve the capability and performance of service operations, while letting customers know that the company is committed to excellence and willing to adhere to global standards.
From a Support perspective, you could say we are fighting above our weight class with our Support capability. This is witnessed by other SCP certified companies including EMC, NetApp, McKesson and Juniper Networks - all considered to have mature Support operations that service large enterprise customers. Having said that most customers would be more interested in the maturity and usability of our products and maturing our product quality practices to gauge us as enterprise.
#11: Cloudera Navigator is the only integrated data management and governance platform for Hadoop.
It is a critical part of Cloudera Enterprise and is trusted in production by hundreds of our customers across multiple industries (regulated and not). With over two years of development, Cloudera was the first Hadoop vendor to introduce a data management and governance solution. Cloudera Navigator is a mature tool that going well beyond auditing and metadata collection.
Cloudera Navigator and data governance is a key part of passing compliance audits. Cloudera is the only Hadoop distribution to pass a compliance audit (PCI-DSS with Mastercard) and Navigator plays a huge part in that
Cloudera Navigator also features interoperability with the broad partner ecosystem. It integrates with the leading tools for data lineage, policies, audits, quality, and more so you can manage data both within the Hadoop platform and beyond.
#17: Invent or distribute variety of useful and diverse workloads
Create architecture to ingest, store, and share data across parallel workloads
Imbue numerous enterprise qualities into those workloads
Make it work reliably and cost effectively in multi-tenant, multiple environments
Self-service for knowledge workers with varying needs and control access
Optimize performance for customer’s production environment
Open source innovation
Multi-cloud – No vendor lock in. Work in the environment of your choice. Better pricing leverage
Managed TCO – Multiple pricing and deployment options
Integrated – Integrated components with shared metadata, security and operations
Secure - Protect sensitive data from unauthorized access – encryption, key management
Compliance – Full auditing and visibility
Governance – Ensure data veracity
#18: Charles: why can you only do this with all data in one place. What would happen? More cumbersome? Expensive? Not at all possible? What do we unlock?
Terry Kline CIO at Navistar: "We have a number of different applications running after our data every day from truck drivers to dealers to parents to students riding the school busses, and Cloudera SDX is key to making that happen at Navistar. SDX is foundational on how we track and govern our data and protect the data of the owner of the truck."
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This multi-function approach helps freight businesses prevent vehicle downtime. They do this by ingesting a variety of telematics data in real time from the fleet of trucks, using machine learning to predict the likelihood that a certain part will fail at a given time, and then running analytics to determine the best way to pull the truck off the road and service it in a manner that minimizes downtime.
Third, experience has also shown that a scalable and consistent security and governance model is a prerequisite for businesses to enable a diverse set of data practitioners to interact with a shared set of sensitive or regulated data.
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Pharmaceutical businesses are working to accelerate drug research programs by providing a self-service analytics experience on a shared pool of data to their entire research team. However, since much of this data is regulated by HIPAA, this more efficient method of drug research would not be possible if the data management team was not able to first ensure that a consistent security and governance model had been applied consistently throughout.
With this in mind, it is clear that the preferred choice for any business should be a platform that provides a reliable implementation of each of these core functions and simultaneously provides a shared data experience to all of the data practitioners operating on that platform. This unified model for enterprise data management is indeed the most cost effective, the fastest to deploy, and the easiest to secure and govern. SDX makes this unified model possible. SDX creates this shared data experience for Cloudera’s customers.