Building an End-to-End Solution in Microsoft Fabric: From Dataverse to Power BI (Presented at SQLSaturday Oregon & SW Washington on November 11th, 2023)
Azure Data Factory ETL Patterns in the CloudMark Kromer
This document discusses ETL patterns in the cloud using Azure Data Factory. It covers topics like ETL vs ELT, the importance of scale and flexible schemas in cloud ETL, and how Azure Data Factory supports workflows, templates, and integration with on-premises and cloud data. It also provides examples of nightly ETL data flows, handling schema drift, loading dimensional models, and data science scenarios using Azure data services.
Microsoft Data Integration Pipelines: Azure Data Factory and SSISMark Kromer
The document discusses tools for building ETL pipelines to consume hybrid data sources and load data into analytics systems at scale. It describes how Azure Data Factory and SQL Server Integration Services can be used to automate pipelines that extract, transform, and load data from both on-premises and cloud data stores into data warehouses and data lakes for analytics. Specific patterns shown include analyzing blog comments, sentiment analysis with machine learning, and loading a modern data warehouse.
Azure Data Factory Data Flows Training v005Mark Kromer
Mapping Data Flow is a new feature of Azure Data Factory that allows building data transformations in a visual interface without code. It provides a serverless, scale-out transformation engine for processing big data with unstructured requirements. Mapping Data Flows can be authored and designed visually, with transformations, expressions, and results previews, and then operationalized with Data Factory scheduling, monitoring, and control flow.
This document is a training presentation on Databricks fundamentals and the data lakehouse concept by Dalibor Wijas from November 2022. It introduces Wijas and his experience. It then discusses what Databricks is, why it is needed, what a data lakehouse is, how Databricks enables the data lakehouse concept using Apache Spark and Delta Lake. It also covers how Databricks supports data engineering, data warehousing, and offers tools for data ingestion, transformation, pipelines and more.
This document discusses best practices for using PySpark. It covers:
- Core concepts of PySpark including RDDs and the execution model. Functions are serialized and sent to worker nodes using pickle.
- Recommended project structure with modules for data I/O, feature engineering, and modeling.
- Writing testable, serializable code with static methods and avoiding non-serializable objects like database connections.
- Tips for testing like unit testing functions and integration testing the full workflow.
- Best practices for running jobs like configuring the Python environment, managing dependencies, and logging to debug issues.
Azure Data Factory Mapping Data Flow allows users to stage and transform data in Azure during a limited preview period beginning in February 2019. Data can be staged from Azure Data Lake Storage, Blob Storage, or SQL databases/data warehouses, then transformed using visual data flows before being landed to staging areas in Azure like ADLS, Blob Storage, or SQL databases. For information, contact [email protected] or visit http://aka.ms/dataflowpreview.
Learn Entity Framework in a day with Code First, Model First and Database FirstJibran Rasheed Khan
Learn Entity Framework in a day with Code First, Model First and Database First
•Introduction to Entity Framework (EF)
•Architecture
•What’s new!
•Different approaches to work with (Code first, Database first and model first)
•Choosing right work model
•Pictorial Tour to each model
•Features & Advantages
•Question & Answer
for any help and understanding feel free to contact
thank you
Migrating Airflow-based Apache Spark Jobs to Kubernetes – the Native WayDatabricks
At Nielsen Identity, we use Apache Spark to process 10’s of TBs of data, running on AWS EMR. We started at a point where Spark was not even supported out-of-the-box by EMR, and today we’re spinning-up clusters with 1000’s of nodes on a daily basis, orchestrated by Airflow. A few months ago, we embarked on a journey to evaluate the option of using Kubernetes as our Spark infrastructure, mainly to reduce operational costs and improve stability (as we heavily rely on Spot Instances for our clusters). To allow us to achieve those goals, we combined the open-sourced GCP Spark-on-K8s operator (https://github.com/GoogleCloudPlatform/spark-on-k8s-operator) with a native Airflow integration we developed and recently contributed back to the Airflow project (https://issues.apache.org/jira/browse/AIRFLOW-6542). Finally, we were able to migrate our existing Airflow DAGs, with minimal changes, from AWS EMR to K8s.
Snowflake + Power BI: Cloud Analytics for EveryoneAngel Abundez
This document discusses architectures for using Snowflake and Power BI together. It begins by describing the benefits of each technology. It then outlines several architectural scenarios for connecting Snowflake to Power BI, including using a Power BI gateway, without a gateway, and connecting to Analysis Services. The document also provides examples of usage scenarios and developer best practices. It concludes with a section on data governance considerations for architectures with and without a Power BI gateway.
Dustin Vannoy presented on using Delta Lake with Azure Databricks. He began with an introduction to Spark and Databricks, demonstrating how to set up a workspace. He then discussed limitations of Spark including lack of ACID compliance and small file problems. Delta Lake addresses these issues with transaction logs for ACID transactions, schema enforcement, automatic file compaction, and performance optimizations like time travel. The presentation included demos of Delta Lake capabilities like schema validation, merging, and querying past versions of data.
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.
Building a Streaming Microservice Architecture: with Apache Spark Structured ...Databricks
As we continue to push the boundaries of what is possible with respect to pipeline throughput and data serving tiers, new methodologies and techniques continue to emerge to handle larger and larger workloads
Talend Interview Questions and Answers | Talend Online Training | Talend Tuto...Edureka!
The document provides 22 multiple choice questions that are frequently asked in Talend interviews. The questions cover topics such as Talend components, job configuration, data integration processes, and big data integration. Correct answers are highlighted to help individuals prepare for Talend technical interviews. The questions assess knowledge of the Talend tool and capabilities for data integration, ETL, and big data processing jobs.
LiquiBase is an open source tool for tracking, managing and applying database changes, where database changes are stored in an XML file called a changelog that is executed to handle different revisions. It aims to provide consistent database changes across environments by managing databases at different states and keeping a history of all changes made through automatic rollback support and ability to effectively manage variable changes. Problems with manual database changes include inconsistent application of changes and databases becoming out of sync between environments.
This presentation discusses why Oracle's Cloud is the best choice for running an Oracle Database in the cloud, in particular an Oracle RAC database. This presentation was first presented during Collaborate18 / #C18LV together with Vishal Singh.
Data & Analytics ReInvent Recap [AWS Basel Meetup - Jan 2023]Chris Bingham
The document discusses several announcements related to Amazon Web Services (AWS) data and analytics services. Some of the key announcements include:
- Zero-ETL integration between Amazon Aurora and Amazon Redshift to eliminate the need for extract, transform, and load processes between the two services.
- Updates to AWS Glue including new engines, data formats, and support for the Cloud Shuffle Service Plugin for Apache Spark.
- Enhancements to Amazon SageMaker such as automated data preparation using machine learning, geospatial modeling capabilities, and shadow testing for machine learning models.
- New services including Amazon DataZone for data discovery and access across organizations, Amazon Omics for genomic data storage and analysis, and AWS
DMU is the new tool introduced by Oracle for database conversion to the Unicode character set. Beside introducing briefly the tool, this session will focus on a real database conversion scenario faced by a customer, the problems encountered and the solutions.
Spark SQL Deep Dive @ Melbourne Spark MeetupDatabricks
This document summarizes a presentation on Spark SQL and its capabilities. Spark SQL allows users to run SQL queries on Spark, including HiveQL queries with UDFs, UDAFs, and SerDes. It provides a unified interface for reading and writing data in various formats. Spark SQL also allows users to express common operations like selecting columns, joining data, and aggregation concisely through its DataFrame API. This reduces the amount of code users need to write compared to lower-level APIs like RDDs.
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Spark SQL Tutorial | Spark Tutorial for Beginners | Apache Spark Training | E...Edureka!
This Edureka Spark SQL Tutorial will help you to understand how Apache Spark offers SQL power in real-time. This tutorial also demonstrates an use case on Stock Market Analysis using Spark SQL. Below are the topics covered in this tutorial:
1) Limitations of Apache Hive
2) Spark SQL Advantages Over Hive
3) Spark SQL Success Story
4) Spark SQL Features
5) Architecture of Spark SQL
6) Spark SQL Libraries
7) Querying Using Spark SQL
8) Demo: Stock Market Analysis With Spark SQL
Virtual Flink Forward 2020: Netflix Data Mesh: Composable Data Processing - J...Flink Forward
Netflix processes trillions of events and petabytes of data a day in the Keystone data pipeline, which is built on top of Apache Flink. As Netflix has scaled up original productions annually enjoyed by more than 150 million global members, data integration across the streaming service and the studio has become a priority. Scalably integrating data across hundreds of different data stores in a way that enables us to holistically optimize cost, performance and operational concerns presented a significant challenge. Learn how we expanded the scope of the Keystone pipeline into the Netflix Data Mesh, our real-time, general-purpose, data transportation platform for moving data between Netflix systems. The Keystone Platform’s unique approach to declarative configuration and schema evolution, as well as our approach to unifying batch and streaming data and processing will be covered in depth.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
The document discusses the challenges of modern data, analytics, and AI workloads. Most enterprises struggle with siloed data systems that make integration and productivity difficult. The future of data lies with a data lakehouse platform that can unify data engineering, analytics, data warehousing, and machine learning workloads on a single open platform. The Databricks Lakehouse platform aims to address these challenges with its open data lake approach and capabilities for data engineering, SQL analytics, governance, and machine learning.
Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Archi...Databricks
Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark’s built-in functions make it easy for developers to express complex computations. Delta Lake, on the other hand, is the best way to store structured data because it is a open-source storage layer that brings ACID transactions to Apache Spark and big data workloads Together, these can make it very easy to build pipelines in many common scenarios. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem that needs to be solved. Apache Spark, being a unified analytics engine doing both batch and stream processing, often provides multiples ways to solve the same problem. So understanding the requirements carefully helps you to architect your pipeline that solves your business needs in the most resource efficient manner.
In this talk, I am going examine a number common streaming design patterns in the context of the following questions.
WHAT are you trying to consume? What are you trying to produce? What is the final output that the business wants? What are your throughput and latency requirements?
WHY do you really have those requirements? Would solving the requirements of the individual pipeline actually solve your end-to-end business requirements?
HOW are going to architect the solution? And how much are you willing to pay for it?
Clarity in understanding the ‘what and why’ of any problem can automatically much clarity on the ‘how’ to architect it using Structured Streaming and, in many cases, Delta Lake.
Data Storage Tips for Optimal Spark Performance-(Vida Ha, Databricks)Spark Summit
Vida Ha presented best practices for storing and working with data in files for optimal Spark performance. Some key tips included choosing appropriate file sizes between 64 MB to 1 GB, using splittable compression formats like gzip and Snappy, enforcing schemas for structured formats like Parquet and Avro, and reusing Hadoop libraries to read various file formats. General tips involved controlling output file size through methods like coalesce and repartition, using sc.wholeTextFiles for non-splittable formats, and processing files individually by filename.
Bulk data loading in Snowflake involves the following steps:
1. Creating file format objects to define file types and formats
2. Creating stage objects to store loaded files
3. Staging data files in the stages
4. Listing the staged files
5. Copying data from the stages into target tables
Webinar with SnagAJob, HP Vertica and Looker - Data at the speed of busines s...Looker
Enterprise companies are struggling to manage increasing demands for data with legacy BI tools. By centralizing their data in Vertica, SnagAJob, an online marketplace for hourly jobs with over 60 million users, can now use Looker to create a single source of truth and put data in the hands of decision-makers across the company.
Horses for Courses: Database RoundtableEric Kavanagh
The blessing and curse of today's database market? So many choices! While relational databases still dominate the day-to-day business, a host of alternatives has evolved around very specific use cases: graph, document, NoSQL, hybrid (HTAP), column store, the list goes on. And the database tools market is teeming with activity as well. Register for this special Research Webcast to hear Dr. Robin Bloor share his early findings about the evolving database market. He'll be joined by Steve Sarsfield of HPE Vertica, and Robert Reeves of Datical in a roundtable discussion with Bloor Group CEO Eric Kavanagh. Send any questions to [email protected], or tweet with #DBSurvival.
Migrating Airflow-based Apache Spark Jobs to Kubernetes – the Native WayDatabricks
At Nielsen Identity, we use Apache Spark to process 10’s of TBs of data, running on AWS EMR. We started at a point where Spark was not even supported out-of-the-box by EMR, and today we’re spinning-up clusters with 1000’s of nodes on a daily basis, orchestrated by Airflow. A few months ago, we embarked on a journey to evaluate the option of using Kubernetes as our Spark infrastructure, mainly to reduce operational costs and improve stability (as we heavily rely on Spot Instances for our clusters). To allow us to achieve those goals, we combined the open-sourced GCP Spark-on-K8s operator (https://github.com/GoogleCloudPlatform/spark-on-k8s-operator) with a native Airflow integration we developed and recently contributed back to the Airflow project (https://issues.apache.org/jira/browse/AIRFLOW-6542). Finally, we were able to migrate our existing Airflow DAGs, with minimal changes, from AWS EMR to K8s.
Snowflake + Power BI: Cloud Analytics for EveryoneAngel Abundez
This document discusses architectures for using Snowflake and Power BI together. It begins by describing the benefits of each technology. It then outlines several architectural scenarios for connecting Snowflake to Power BI, including using a Power BI gateway, without a gateway, and connecting to Analysis Services. The document also provides examples of usage scenarios and developer best practices. It concludes with a section on data governance considerations for architectures with and without a Power BI gateway.
Dustin Vannoy presented on using Delta Lake with Azure Databricks. He began with an introduction to Spark and Databricks, demonstrating how to set up a workspace. He then discussed limitations of Spark including lack of ACID compliance and small file problems. Delta Lake addresses these issues with transaction logs for ACID transactions, schema enforcement, automatic file compaction, and performance optimizations like time travel. The presentation included demos of Delta Lake capabilities like schema validation, merging, and querying past versions of data.
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.
Building a Streaming Microservice Architecture: with Apache Spark Structured ...Databricks
As we continue to push the boundaries of what is possible with respect to pipeline throughput and data serving tiers, new methodologies and techniques continue to emerge to handle larger and larger workloads
Talend Interview Questions and Answers | Talend Online Training | Talend Tuto...Edureka!
The document provides 22 multiple choice questions that are frequently asked in Talend interviews. The questions cover topics such as Talend components, job configuration, data integration processes, and big data integration. Correct answers are highlighted to help individuals prepare for Talend technical interviews. The questions assess knowledge of the Talend tool and capabilities for data integration, ETL, and big data processing jobs.
LiquiBase is an open source tool for tracking, managing and applying database changes, where database changes are stored in an XML file called a changelog that is executed to handle different revisions. It aims to provide consistent database changes across environments by managing databases at different states and keeping a history of all changes made through automatic rollback support and ability to effectively manage variable changes. Problems with manual database changes include inconsistent application of changes and databases becoming out of sync between environments.
This presentation discusses why Oracle's Cloud is the best choice for running an Oracle Database in the cloud, in particular an Oracle RAC database. This presentation was first presented during Collaborate18 / #C18LV together with Vishal Singh.
Data & Analytics ReInvent Recap [AWS Basel Meetup - Jan 2023]Chris Bingham
The document discusses several announcements related to Amazon Web Services (AWS) data and analytics services. Some of the key announcements include:
- Zero-ETL integration between Amazon Aurora and Amazon Redshift to eliminate the need for extract, transform, and load processes between the two services.
- Updates to AWS Glue including new engines, data formats, and support for the Cloud Shuffle Service Plugin for Apache Spark.
- Enhancements to Amazon SageMaker such as automated data preparation using machine learning, geospatial modeling capabilities, and shadow testing for machine learning models.
- New services including Amazon DataZone for data discovery and access across organizations, Amazon Omics for genomic data storage and analysis, and AWS
DMU is the new tool introduced by Oracle for database conversion to the Unicode character set. Beside introducing briefly the tool, this session will focus on a real database conversion scenario faced by a customer, the problems encountered and the solutions.
Spark SQL Deep Dive @ Melbourne Spark MeetupDatabricks
This document summarizes a presentation on Spark SQL and its capabilities. Spark SQL allows users to run SQL queries on Spark, including HiveQL queries with UDFs, UDAFs, and SerDes. It provides a unified interface for reading and writing data in various formats. Spark SQL also allows users to express common operations like selecting columns, joining data, and aggregation concisely through its DataFrame API. This reduces the amount of code users need to write compared to lower-level APIs like RDDs.
Building an Effective Data Warehouse ArchitectureJames Serra
Why use a data warehouse? What is the best methodology to use when creating a data warehouse? Should I use a normalized or dimensional approach? What is the difference between the Kimball and Inmon methodologies? Does the new Tabular model in SQL Server 2012 change things? What is the difference between a data warehouse and a data mart? Is there hardware that is optimized for a data warehouse? What if I have a ton of data? During this session James will help you to answer these questions.
Spark SQL Tutorial | Spark Tutorial for Beginners | Apache Spark Training | E...Edureka!
This Edureka Spark SQL Tutorial will help you to understand how Apache Spark offers SQL power in real-time. This tutorial also demonstrates an use case on Stock Market Analysis using Spark SQL. Below are the topics covered in this tutorial:
1) Limitations of Apache Hive
2) Spark SQL Advantages Over Hive
3) Spark SQL Success Story
4) Spark SQL Features
5) Architecture of Spark SQL
6) Spark SQL Libraries
7) Querying Using Spark SQL
8) Demo: Stock Market Analysis With Spark SQL
Virtual Flink Forward 2020: Netflix Data Mesh: Composable Data Processing - J...Flink Forward
Netflix processes trillions of events and petabytes of data a day in the Keystone data pipeline, which is built on top of Apache Flink. As Netflix has scaled up original productions annually enjoyed by more than 150 million global members, data integration across the streaming service and the studio has become a priority. Scalably integrating data across hundreds of different data stores in a way that enables us to holistically optimize cost, performance and operational concerns presented a significant challenge. Learn how we expanded the scope of the Keystone pipeline into the Netflix Data Mesh, our real-time, general-purpose, data transportation platform for moving data between Netflix systems. The Keystone Platform’s unique approach to declarative configuration and schema evolution, as well as our approach to unifying batch and streaming data and processing will be covered in depth.
Architect’s Open-Source Guide for a Data Mesh ArchitectureDatabricks
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
The document discusses the challenges of modern data, analytics, and AI workloads. Most enterprises struggle with siloed data systems that make integration and productivity difficult. The future of data lies with a data lakehouse platform that can unify data engineering, analytics, data warehousing, and machine learning workloads on a single open platform. The Databricks Lakehouse platform aims to address these challenges with its open data lake approach and capabilities for data engineering, SQL analytics, governance, and machine learning.
Designing ETL Pipelines with Structured Streaming and Delta Lake—How to Archi...Databricks
Structured Streaming has proven to be the best platform for building distributed stream processing applications. Its unified SQL/Dataset/DataFrame APIs and Spark’s built-in functions make it easy for developers to express complex computations. Delta Lake, on the other hand, is the best way to store structured data because it is a open-source storage layer that brings ACID transactions to Apache Spark and big data workloads Together, these can make it very easy to build pipelines in many common scenarios. However, expressing the business logic is only part of the larger problem of building end-to-end streaming pipelines that interact with a complex ecosystem of storage systems and workloads. It is important for the developer to truly understand the business problem that needs to be solved. Apache Spark, being a unified analytics engine doing both batch and stream processing, often provides multiples ways to solve the same problem. So understanding the requirements carefully helps you to architect your pipeline that solves your business needs in the most resource efficient manner.
In this talk, I am going examine a number common streaming design patterns in the context of the following questions.
WHAT are you trying to consume? What are you trying to produce? What is the final output that the business wants? What are your throughput and latency requirements?
WHY do you really have those requirements? Would solving the requirements of the individual pipeline actually solve your end-to-end business requirements?
HOW are going to architect the solution? And how much are you willing to pay for it?
Clarity in understanding the ‘what and why’ of any problem can automatically much clarity on the ‘how’ to architect it using Structured Streaming and, in many cases, Delta Lake.
Data Storage Tips for Optimal Spark Performance-(Vida Ha, Databricks)Spark Summit
Vida Ha presented best practices for storing and working with data in files for optimal Spark performance. Some key tips included choosing appropriate file sizes between 64 MB to 1 GB, using splittable compression formats like gzip and Snappy, enforcing schemas for structured formats like Parquet and Avro, and reusing Hadoop libraries to read various file formats. General tips involved controlling output file size through methods like coalesce and repartition, using sc.wholeTextFiles for non-splittable formats, and processing files individually by filename.
Bulk data loading in Snowflake involves the following steps:
1. Creating file format objects to define file types and formats
2. Creating stage objects to store loaded files
3. Staging data files in the stages
4. Listing the staged files
5. Copying data from the stages into target tables
Webinar with SnagAJob, HP Vertica and Looker - Data at the speed of busines s...Looker
Enterprise companies are struggling to manage increasing demands for data with legacy BI tools. By centralizing their data in Vertica, SnagAJob, an online marketplace for hourly jobs with over 60 million users, can now use Looker to create a single source of truth and put data in the hands of decision-makers across the company.
Horses for Courses: Database RoundtableEric Kavanagh
The blessing and curse of today's database market? So many choices! While relational databases still dominate the day-to-day business, a host of alternatives has evolved around very specific use cases: graph, document, NoSQL, hybrid (HTAP), column store, the list goes on. And the database tools market is teeming with activity as well. Register for this special Research Webcast to hear Dr. Robin Bloor share his early findings about the evolving database market. He'll be joined by Steve Sarsfield of HPE Vertica, and Robert Reeves of Datical in a roundtable discussion with Bloor Group CEO Eric Kavanagh. Send any questions to [email protected], or tweet with #DBSurvival.
Cherokee nation 2 day AIAD & DIAD - App in a day and Dashboard in dayVishal Pawar
Cherokee nation 2 day AIAD & DIAD - App in a day and Dashboard in day
Power Apps: A software as a service application platform that enables power users in line of business
roles to easily build and deploy custom business apps. You will learn how to build Canvas and Modeldriven
style of apps.
Common Data Service (CDS): Make it easier to bring your data together and quickly create powerful
apps using a compliant and scalable data service and app platform that’s integrated into Power Apps.
Power Automate: A business service for line of business specialists and IT pros to build automated
workflows intuitively.
Power BI: Self-service business intelligence capabilities, where end users can create reports and
dashboards by themselves, without having to depend on information technology staff or database
administrators.
The slide deck for the Power Platform Presentation in SQL Saturday Redmond 2019. We have reviewed the power Platform Components, why is it better together and how to make it happen. During the demo all the options of implementation between the Power Apps and PowerBI were demonstrated. Including the data visualization changes with new data feed. Use some of the following ideas in your organization and POC's for more complex implementations.
- The document discusses real-time options in Power BI including push, streaming, and PubNub data. It describes the characteristics of each option including refresh rates, visual capabilities, and advantages/limitations.
- A case study is presented on creating a dashboard to monitor warehouse workload in real-time using a hybrid dataset with data pushed from SQL Server and SAP HANA via REST APIs into Power BI. PowerApps is also suggested for creating mobile apps connected to the real-time data.
- Additional resources are provided on real-time streaming documentation, tutorials for IoT dashboards and connecting Azure Stream Analytics, and using PubNub streams in Power BI.
This document describes a 5-day training program on Power BI tools. Each day focuses on a different topic, from data modeling to visualizations to deployment. The goal is to help organizations develop prototype BI solutions using their own data to drive business decisions. Trainees will learn to create, manage and deploy data exploration solutions leveraging tools like Power Pivot, Power View and Power Map. The scope of the solution created is typically limited to 3 data sources and 5 Power View reports due to time constraints, but post-engagement support is provided.
The document discusses Azure Data Factory v2. It provides an agenda that includes topics like triggers, control flow, and executing SSIS packages in ADFv2. It then introduces the speaker, Stefan Kirner, who has over 15 years of experience with Microsoft BI tools. The rest of the document consists of slides on ADFv2 topics like the pipeline model, triggers, activities, integration runtimes, scaling SSIS packages, and notes from the field on using SSIS packages in ADFv2.
The document provides an overview of leading big data companies in 2021 and the Apache Hadoop stack, including related Apache software and the NIST big data reference architecture. It lists over 50 big data companies, including Accenture, Actian, Aerospike, Alluxio, Amazon Web Services, Cambridge Semantics, Cloudera, Cloudian, Cockroach Labs, Collibra, Couchbase, Databricks, DataKitchen, DataStax, Denodo, Dremio, Franz, Gigaspaces, Google Cloud, GridGain, HPE, HVR, IBM, Immuta, InfluxData, Informatica, IRI, MariaDB, Matillion, Melissa Data
6 steps to richer visualizations using alteryx for microsoft power bi updatedPhillip Reinhart
Microsoft Power BI enables analysts to deliver incredible data-driven insights
and visualizations to their organizations. As decision makers recognize the value
of visual analytics produced in Microsoft Power BI, analysts must find ways
of dealing with the increasing volumes and complexity of the data required to
get to these insights and visualizations. For Microsoft Power BI users this is a
critical and often time consuming process. A lot of time spent revolves around
blending data from multiple sources to create an actionable analytic dataset.
Hence, this forces analysts to spend many days dealing with:
• Wasted time waiting for others to get them the right data for their analysis
• Manual preparation and integration of different data sets
• A lack of advanced analytics that many decisions require
Alteryx provides the advanced data blending capabilities required to reduce
the time and effort to create the perfect dataset for a Microsoft Power BI
visualization. This cookbook shows you how you can quickly blend multiple
sources of data in order to create richer visualizations in Microsoft Power BI.
DataOps - The Foundation for Your Agile Data ArchitectureDATAVERSITY
Achieving agility in data and analytics is hard. It’s no secret that most data organizations struggle to deliver the on-demand data products that their business customers demand. Recently, there has been much hype around new design patterns that promise to deliver this much sought-after agility.
In this webinar, Chris Bergh, CEO and Head Chef of DataKitchen will cut through the noise and describe several elegant and effective data architecture design patterns that deliver low errors, rapid development, and high levels of collaboration. He’ll cover:
• DataOps, Data Mesh, Functional Design, and Hub & Spoke design patterns;
• Where Data Fabric fits into your architecture;
• How different patterns can work together to maximize agility; and
• How a DataOps platform serves as the foundational superstructure for your agile architecture.
Analytics at the Speed of Thought: Actian Express Overview Actian Corporation
Deliver faster insight – reduce query response times to seconds
Analyze more data faster – explore billions of rows of data in seconds
More concurrent users – enable more concurrent BI users to explore more data
Power BI Full Course | Power BI Tutorial for Beginners | EdurekaEdureka!
YouTube Link: https://youtu.be/3u7MQz1EyPY
** Power BI Training - https://www.edureka.co/power-bi-training **
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The document provides an overview of data mesh principles and hands-on examples for implementing a data mesh. It discusses key concepts of a data mesh including data ownership by domain, treating data as a product, making data available everywhere through self-service, and federated governance of data wherever it resides. Hands-on examples are provided for creating a data mesh topology with Apache Kafka as the underlying infrastructure, developing data products within domains, and exploring consumption of real-time and historical data from the mesh.
2017 09-27 democratize data products with SQLYu Ishikawa
The document discusses building scalable data products using SQL and cloud technologies. It proposes using Google BigQuery for scalable data analytics with SQL, exporting the BigQuery table to Google Datastore for a relational-style database using Apache Beam and Google Dataflow. This allows creating scalable data products without having to manage scalability or implementations, just by executing a command. An example counts page views and unique users by item from event logs in BigQuery, exports it to Datastore, avoiding complex distributed processing frameworks for simple cases.
Microsoft Fabric is the next version of Azure Data Factory, Azure Data Explorer, Azure Synapse Analytics, and Power BI. It brings all of these capabilities together into a single unified analytics platform that goes from the data lake to the business user in a SaaS-like environment. Therefore, the vision of Fabric is to be a one-stop shop for all the analytical needs for every enterprise and one platform for everyone from a citizen developer to a data engineer. Fabric will cover the complete spectrum of services including data movement, data lake, data engineering, data integration and data science, observational analytics, and business intelligence. With Fabric, there is no need to stitch together different services from multiple vendors. Instead, the customer enjoys end-to-end, highly integrated, single offering that is easy to understand, onboard, create and operate.
This is a hugely important new product from Microsoft and I will simplify your understanding of it via a presentation and demo.
Agenda:
What is Microsoft Fabric?
Workspaces and capacities
OneLake
Lakehouse
Data Warehouse
ADF
Power BI / DirectLake
Resources
Accelerating Data Lakes and Streams with Real-time AnalyticsArcadia Data
As organizations modernize their data and analytics platforms, the data lake concept has gained momentum as a shared enterprise resource for supporting insights across multiple lines of business. The perception is that data lakes are vast, slow-moving bodies of data, but innovations like Apache Kafka for streaming-first architectures put real-time data flows at the forefront. Combining real-time alerts and fast-moving data with rich historical analysis lets you respond quickly to changing business conditions with powerful data lake analytics to make smarter decisions.
Join this complimentary webinar with industry experts from 451 Research and Arcadia Data who will discuss:
- Business requirements for combining real-time streaming and ad hoc visual analytics.
- Innovations in real-time analytics using tools like Confluent’s KSQL.
- Machine-assisted visualization to guide business analysts to faster insights.
- Elevating user concurrency and analytic performance on data lakes.
- Applications in cybersecurity, regulatory compliance, and predictive maintenance on manufacturing equipment all benefit from streaming visualizations.
ADV Slides: 2021 Trends in Enterprise AnalyticsDATAVERSITY
The document discusses trends in enterprise advanced analytics for 2021 and beyond. Some key trends include remote work continuing, strong tech spending rebound led by cloud capabilities, leading organizations increasing focus on AI/ML with model deployment taking center stage, more edge AI, rise of data lakes, new technology stacks focusing on data fabrics and AI pipelines, increased automation, open source becoming more prevalent, Kubernetes becoming the standard analytics stack, and general AI beginning to emerge. Winning approaches for 2021 include cloud, AI, data lakes, data warehousing, MDM, agile development, Kubernetes, automation, data quality, and DevOps/MLOps.
Load data from Quickbook to Snowflake in minutessyed_javed
Modern data solution like Lyftron enables data governance with data catalog, data model, data definition, data lineage, tagging and enterprise data dictionary search.
HiFX designed and implemented a unified data analytics platform called Vision Lens for Malayala Manorama to generate meaningful insights from large amounts of data across their multiple digital properties. The solution involved building a data lake, data pipeline, processing framework, and dashboards to provide real-time and historical analytics. This helped Manorama improve user experiences, drive smarter marketing, and make better business decisions.
The Heart of the Data Mesh Beats in Real-Time with Apache KafkaKai Wähner
If there were a buzzword of the hour, it would certainly be "data mesh"! This new architectural paradigm unlocks analytic data at scale and enables rapid access to an ever-growing number of distributed domain datasets for various usage scenarios.
As such, the data mesh addresses the most common weaknesses of the traditional centralized data lake or data platform architecture. And the heart of a data mesh infrastructure must be real-time, decoupled, reliable, and scalable.
This presentation explores how Apache Kafka, as an open and scalable decentralized real-time platform, can be the basis of a data mesh infrastructure and - complemented by many other data platforms like a data warehouse, data lake, and lakehouse - solve real business problems.
There is no silver bullet or single technology/product/cloud service for implementing a data mesh. The key outcome of a data mesh architecture is the ability to build data products; with the right tool for the job.
A good data mesh combines data streaming technology like Apache Kafka or Confluent Cloud with cloud-native data warehouse and data lake architectures from Snowflake, Databricks, Google BigQuery, et al.
Website Analytics in My Pocket using Microsoft Fabric (SQLBits 2024)Cathrine Wilhelmsen
The document is about how the author Cathrine Wilhelmsen built her own website analytics dashboard using Microsoft Fabric and Power BI. She collects data from the Cloudflare API and stores it in Microsoft Fabric. This allows her to visualize and access the analytics data on her phone through a mobile app beyond the 30 days retention offered by Cloudflare. In her presentation, she demonstrates how she retrieves the website data, processes it with Microsoft Fabric pipelines, and visualizes it in Power BI for a self-hosted analytics solution.
Data Integration with Data Factory (Microsoft Fabric Day Oslo 2023)Cathrine Wilhelmsen
Cathrine Wilhelmsen gave a presentation on using Microsoft Data Factory for data integration within Microsoft Fabric. Data Factory allows users to define data pipelines to ingest, transform and orchestrate data workflows. Pipelines contain activities that can copy or move data between different data stores. Connections specify how to connect to these data stores. Dataflows Gen2 provide enhanced orchestration capabilities, including defining activity dependencies and schedules. The presentation demonstrated how to use these capabilities in Data Factory for complex data integration scenarios.
The Battle of the Data Transformation Tools (PASS Data Community Summit 2023)Cathrine Wilhelmsen
The Battle of the Data Transformation Tools (Presented as part of the "Batte of the Data Transformation Tools" Learning Path at PASS Data Community Summit on November 16th, 2023)
Visually Transform Data in Azure Data Factory or Azure Synapse Analytics (PAS...Cathrine Wilhelmsen
Visually Transform Data in Azure Data Factory or Azure Synapse Analytics (Presented as part of the "Batte of the Data Transformation Tools" Learning Path at PASS Data Community Summit on November 15th, 2023)
Website Analytics in my Pocket using Microsoft Fabric (AdaCon 2023)Cathrine Wilhelmsen
The document is about how the author created a mobile-friendly dashboard for her website analytics using Microsoft Fabric and Power BI. She collects data from the Cloudflare API and stores it in Microsoft Fabric. Then she visualizes the data in Power BI which can be viewed on her phone. This allows her to track website traffic and see which pages are most popular over time. She demonstrates her dashboard and discusses future improvements like comparing statistics across different time periods.
Stressed, Depressed, or Burned Out? The Warning Signs You Shouldn't Ignore (S...Cathrine Wilhelmsen
Stressed, Depressed, or Burned Out? The Warning Signs You Shouldn't Ignore (Presented at SQLBits on March 18th, 2023)
We all experience stress in our lives. When the stress is time-limited and manageable, it can be positive and productive. This kind of stress can help you get things done and lead to personal growth. However, when the stress stretches out over longer periods of time and we are unable to manage it, it can be negative and debilitating. This kind of stress can affect your mental health as well as your physical health, and increase the risk of depression and burnout.
The tricky part is that both depression and burnout can hit you hard without the warning signs you might recognize from stress. Where stress barges through your door and yells "hey, it's me!", depression and burnout can silently sneak in and gradually make adjustments until one day you turn around and see them smiling while realizing that you no longer recognize your house. I know, because I've dealt with both. And when I thought I had kicked them out, they both came back for new visits.
I don't have the Answers™️ or Solutions™️ to how to keep them away forever. But in hindsight, there were plenty of warning signs I missed, ignored, or was oblivious to at the time. In this deeply personal session, I will share my story of dealing with both depression and burnout. What were the warning signs? Why did I miss them? Could I have done something differently? And most importantly, what can I - and you - do to help ourselves or our loved ones if we notice that something is not quite right?
"I can't keep up!" - Turning Discomfort into Personal Growth in a Fast-Paced ...Cathrine Wilhelmsen
"I can't keep up!" - Turning Discomfort into Personal Growth in a Fast-Paced World (Presented at SQLBits on March 17th, 2023)
Do you sometimes think the world is moving so fast that you're struggling to keep up?
Does it make you feel a little uncomfortable?
Awesome!
That means that you have ambitions. You want to learn new things, take that next step in your career, achieve your goals. You can do anything if you set your mind to it.
It just might not be easy.
All growth requires some discomfort. You need to manage and balance that discomfort, find a way to push yourself a little bit every day without feeling overwhelmed. In a fast-paced world, you need to know how to break down your goals into smaller chunks, how to prioritize, and how to optimize your learning.
Are you ready to turn your "I can't keep up" into "I can't believe I did all of that in just one year"?
Lessons Learned: Implementing Azure Synapse Analytics in a Rapidly-Changing S...Cathrine Wilhelmsen
Lessons Learned: Implementing Azure Synapse Analytics in a Rapidly-Changing Startup (Presented at SQLBits on March 11th, 2022)
What happens when you mix one rapidly-changing startup, one data analyst, one data engineer, and one hypothesis that Azure Synapse Analytics could be the right tool of choice for gaining business insights?
We had no idea, but we gave it a go!
Our ambition was to think big, start small, and act fast – to deliver business value early and often.
Did we succeed?
Join us for an honest conversation about why we decided to implement Azure Synapse Analytics alongside Power BI, how we got started, which areas we completely messed up at first, what our current solution looks like, the lessons learned along the way, and the things we would have done differently if we could start all over again.
6 Tips for Building Confidence as a Public Speaker (SQLBits 2022)Cathrine Wilhelmsen
6 Tips for Building Confidence as a Public Speaker (Presented at SQLBits on March 10th, 2022)
Do you feel nervous about getting on stage to deliver a presentation?
That was me a few years ago. Palms sweating. Hands shaking. Voice trembling. I could barely breathe and talked at what felt like a thousand words per second. Now, public speaking is one of my favorite hobbies. Sometimes, I even plan my vacations around events! What changed?
There are no shortcuts to building confidence as a public speaker. However, there are many things you can do to make the journey a little easier for yourself. In this session, I share the top tips I have learned over the years. All it takes is a little preparation and practice.
You can do this!
Understanding Azure Data Factory: The What, When, and Why (NIC 2020)Cathrine Wilhelmsen
The document is a presentation on Azure Data Factory that discusses what it is, when and why it would be used, and how to work with it. It defines Azure Data Factory as a data integration service that can copy and transform data. It demonstrates how to use Azure Data Factory to copy data between cloud and on-premises data stores, transform data using mapping and wrangling data flows, and schedule data pipelines using triggers. Common data architectures that use Azure Data Factory are also presented.
How iCode cybertech Helped Me Recover My Lost Fundsireneschmid345
I was devastated when I realized that I had fallen victim to an online fraud, losing a significant amount of money in the process. After countless hours of searching for a solution, I came across iCode cybertech. From the moment I reached out to their team, I felt a sense of hope that I can recommend iCode Cybertech enough for anyone who has faced similar challenges. Their commitment to helping clients and their exceptional service truly set them apart. Thank you, iCode cybertech, for turning my situation around!
[email protected]
GenAI for Quant Analytics: survey-analytics.aiInspirient
Pitched at the Greenbook Insight Innovation Competition as apart of IIEX North America 2025 on 30 April 2025 in Washington, D.C.
Join us at survey-analytics.ai!
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.
3. Microsoft Fabric is the newest and coolest kid in town and for good reasons. It brings together the
best features from Azure Data Factory, Azure Synapse Analytics and Power BI, making it easier than
ever to build analytical solutions. Join us as we walk through how to build an end-to-end lakehouse
solution in just one hour!
In this session, we will show you how to use Synapse Link to get near real-time data from Dataverse
and leverage the power of Notebooks to prepare and optimize the data for the lakehouse medallion
architecture. Then, we will use that data to build stunning visualizations in Power BI that provide
valuable insights. Finally, we will introduce the new Data Activator tool, an innovative component of
Microsoft Fabric that empowers business analysts to drive actions based on data automatically.
Don't miss this opportunity to experience Microsoft Fabric's game-changing potential firsthand and
learn how it can revolutionize your analytics workflow.
Building an End-to-End Solution in Microsoft Fabric:
From Dataverse to Power BI
4. Cathrine Wilhelmsen Emilie Rønning
Solutions Architect and Tech Lead
Evidi
@cathrinew
• The OG of anything in the
Norwegian data community
• Fabric February
Azure, Fabric, Azure Data Factory,
Azure Synapse Analytics
Senior Data Engineer
QUARKS
@EmilieRonning
• Data Saturday Oslo
• MDPUG Norway
• Fabric February
Azure, Databricks, Azure
Data Factory, Architecture
Managing Data Analyst
Sopra Steria
@mmoengen
• Data Saturday Oslo
• MDPUG Norway
• WITs Who Lunch
• Fabric February
Azure, Architecture,
Databricks, Governance,
MDM, Fabric, Power BI
Marthe Moengen
5. Our client has a website
where hosts can list their
homes for short-term or
long-term stays.
What does our client need?
They need to be able to:
• Manage Hosts
• Gain insight into stays
Let’s call them «AirBnB»
9. Fabric Components
Data Engineering: Store big data in
lakehouses for querying, reporting and sharing.
Data Warehousing: Store relational data for
querying, reporting and sharing.
Data Science: Use machine learning to detect
trends, identify outliers and predict values.
Real-Time Analytics: Rapidly capture and load
streaming data for querying.
Data Factory: Ingest, clean, transform and
prepare data.
Power BI: Create reports and dashboards to
gain insights.
Data Activator: Create reports and
dashboards to gain insights.
10. Our Solution
1. Add / edit data in Power App
2. Store data in Dataverse
3. Replicate data to OneLake
using Synapse Link
End-to-End
Dataverse OneLake Lakehouse Power BI Data Activator
Power Apps
4. Transform data in Lakehouse
5. Visualize data in Power BI
6. Take action on data
using Data Activator
12. Our Solution
1. Add / edit data in Power App
2. Store data in Dataverse
3. Replicate data to OneLake
using Synapse Link
End-to-End
Dataverse OneLake Lakehouse Power BI Data Activator
Power Apps
4. Transform data in Lakehouse
5. Visualize data in Power BI
6. Take action on data
using Data Activator
Cathrine
17. Our Solution
1. Add / edit data in Power App
2. Store data in Dataverse
3. Replicate data to OneLake
using Synapse Link
End-to-End
Dataverse OneLake Lakehouse Power BI Data Activator
Power Apps
4. Transform data in Lakehouse
5. Visualize data in Power BI
6. Take action on data
using Data Activator
Emilie
18. Our Solution
1. Add / edit data in Power App
2. Store data in Dataverse
3. Replicate data to OneLake
using Synapse Link
End-to-End
Dataverse OneLake Lakehouse Power BI Data Activator
Power Apps
4. Transform data in Lakehouse
5. Visualize data in Power BI
6. Take action on data
using Data Activator
Emilie
20. Transforming Data
Microsoft Fabric Lakehouse
“A data architecture platform
for storing, managing, and
analyzing structured and
unstructured data in a single
location”
21. Overview of Lakehouse Medallion Architecture
The landing
zone for all
data, stored in
its original
format
BRONZE
The layer of
validated and
refined data
SILVER
The layer of enriched
data specified for
business and analytics
needs
GOLD
24. Our Solution
1. Add / edit data in Power App
2. Store data in Dataverse
3. Replicate data to OneLake
using Synapse Link
End-to-End
Dataverse OneLake Lakehouse Power BI Data Activator
Power Apps
4. Transform data in Lakehouse
5. Visualize data in Power BI
6. Take action on data
using Data Activator
Marthe
25. Our Solution
1. Add / edit data in Power App
2. Store data in Dataverse
3. Replicate data to OneLake
using Synapse Link
End-to-End
Dataverse OneLake Lakehouse Power BI Data Activator
Power Apps
4. Transform data in Lakehouse
5. Visualize data in Power BI
6. Take action on data
using Data Activator
Marthe
26. Visualize data in Power BI
Model our
data
First we need to: Second we need to:
Visualize our data
29. Our Solution
1. Add / edit data in Power App
2. Store data in Dataverse
3. Replicate data to OneLake
using Synapse Link
End-to-End
Dataverse OneLake Lakehouse Power BI Data Activator
Power Apps
4. Transform data in Lakehouse
5. Visualize data in Power BI
6. Take action on data
using Data Activator
Marthe
30. How can I automate monitoring and
prompt action?
31. Take Action on Data with Data Activator
Core concepts
What to do
Objects
What you
want to
monitor
Triggers
What to
detect on
the object
Events
Data is
being
updated
Properties
Reuse
logic
Select Detect
Create
a reflex
Act