Big Data in the Cloud with Azure Marketplace Images

Mar 31, 2016Download as PPTX, PDF0 likes639 views

The document discusses strategies for modern data warehousing and analytics on Azure including using Hadoop for ETL/ELT, integrating streaming data engines, and using lambda and hybrid architectures. It also describes using data lakes on Azure to collect and analyze large amounts of data from various sources. Additionally, it covers performing real-time stream analytics, machine learning, and statistical analysis on the data and discusses how Azure provides scalability, speed of deployment, and support for polyglot environments that incorporate many data processing and storage options.

ModernDataWarehouse
• Hadoop as an
ETL/ELT
optimization engine
• Integration of
streaming engines
for data ingestion
• Lambda and Hybrid
architectures that
include multiple
processing and
storage layers
DataLakes
• A good starting
point for an
organization to
enter into Big Data
• Collect data points
as files in a
massively
distributed file
system for analysis,
processing and
warehousing
AdvancedAnalytics
• Real-time stream-
based analytics
• Machine Learning
• Leverage statistics-
based packages in
R and Python for
exploratory analysis
• Uncover the
valuable data
points from Data
Lake

Scale
• Elasticity. Don’t
engineer for worst-
case scenario all the
time.
• Moving data to
Azure means taking
advantage of
Microsoft’s
bottomless storage
and enormous
compute resources.
SpeedofDeployment
• Customers want to
take advantage of
Azure’s ability to
automate
deployments
• A huge advantage of
Cloud Data solutions
in Azure is to quickly
spin-up & spin-
down environments
in dev/test, QA,
production as well as
pause & stop
PolyglotEnvironments
• Azure Data solutions
are able to
incorporate complex
heterogeneous data
environments that
include a large
spectrum of engine
and storage options
from RDBMS to
NoSQL to Hadoop
• Take advantage of
the Azure
Marketplace

Big Data in the Cloud with Azure Marketplace Images

Big Data Analytics in the Cloud using Microsoft Azure services was discussed. Key points included: 1) Azure provides tools for collecting, processing, analyzing and visualizing big data including Azure Data Lake, HDInsight, Data Factory, Machine Learning, and Power BI. These services can be used to build solutions for common big data use cases and architectures. 2) U-SQL is a language for preparing, transforming and analyzing data that allows users to focus on the what rather than the how of problems. It uses SQL and C# and can operate on structured and unstructured data. 3) Visual Studio provides an integrated environment for authoring, debugging, and monitoring U-SQL scripts and jobs. This allows

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The document discusses data architecture solutions for solving real-time, high-volume data problems with low latency response times. It recommends a data platform capable of capturing, ingesting, streaming, and optionally storing data for batch analytics. The solution should provide fast data ingestion, real-time analytics, fast action, and quick time to value. Multiple data sources like logs, social media, and internal systems would be ingested using Apache Flume and Kafka and analyzed with Spark/Storm streaming. The processed data would be stored in HDFS, Cassandra, S3, or Hive. Kafka, Spark, and Cassandra are identified as key technologies for real-time data pipelines, stream analytics, and high availability persistent storage.

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Enterprises are beginning to consider the deployment of data science and data warehouse platforms on hybrid (public cloud, private cloud, and on premises) infrastructure. This delivers the flexibility and freedom of choice to deploy your analytics anywhere you need it and to create an adaptable and agile analytics platform. But the market is conspiring against customer desire for innovation... Leading public cloud vendors are interested in pushing their new, but proprietary, analytic stacks, locking customers into subpar Analytics as a Service (AaaS) for years to come. In tandem, Legacy Data Warehouse vendors are trying to extend the lifecycle of their costly and aging appliances with new features of marginal value, simply imitating the same limiting models of public cloud vendors. New vendors are coming up with interesting ideas, but these ideas are often lacking critical features that don’t provide support for hybrid solutions, limiting the immediate value to users. It is 2017—you can, in fact, have your analytics cake and eat it too! Solve your short term costs and capabilities challenges, and establish a long term hybrid data strategy by running the same open source analytics platform on your infrastructure as it exists today. In this webinar you will learn how Pivotal can help you build a modern analytical architecture able to run on your public, private cloud, or on-premises platform of your choice, while fully leveraging proven open source technologies and supporting the needs of diverse analytical users. Let’s have a productive discussion about how to deploy a solid cloud analytics strategy. Presenter : Jacque Istok, Head of Data Technical Field for Pivotal https://content.pivotal.io/webinars/jul-20-how-to-build-modern-data-architectures-both-on-premises-and-in-the-cloud

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This document provides performance tips for pipelines and copy activities in Azure Data Factory (ADF). It discusses: - Using pipelines for data orchestration with conditional execution and parallel activities. - The Copy activity provides massive-scale data movement within pipelines. Using Copy for ELT can land data quickly into a data lake. - Gaining more throughput by using multiple parallel Copy activities but this can overload the source. - Optimizing copy performance by using binary format, file lists/folders instead of individual files, and SQL source partitioning. - Metrics showing copying Parquet files to a lakehouse at 5.1 GB/s while CSV and SQL loads were slower due to transformation. The

Build data quality rules and data cleansing into your data pipelinesMark Kromer

This document provides guidance on building data quality rules and data cleansing into data pipelines. It discusses considerations for data quality in data warehouse and data science scenarios, including verifying data types and lengths, handling null values, domain value constraints, and reference data lookups. It also provides examples of techniques for replacing values, splitting data based on values, data profiling, pattern matching, enumerations/lookups, de-duplicating data, fuzzy joins, validating metadata rules, and using assertions.

Mapping Data Flows Training deck Q1 CY22Mark Kromer

Mapping data flows allow for code-free data transformation at scale using an Apache Spark engine within Azure Data Factory. Key points: - Mapping data flows can handle structured and unstructured data using an intuitive visual interface without needing to know Spark, Scala, Python, etc. - The data flow designer builds a transformation script that is executed on a JIT Spark cluster within ADF. This allows for scaled-out, serverless data transformation. - Common uses of mapping data flows include ETL scenarios like slowly changing dimensions, analytics tasks like data profiling, cleansing, and aggregations.

Data cleansing and prep with synapse data flowsMark Kromer

This document provides resources for data cleansing and preparation using Azure Synapse Analytics Data Flows. It includes links to videos, documentation, and a slide deck that explain how to use Data Flows for tasks like deduplicating null values, saving data profiler summary statistics, and using metadata functions. A GitHub link shares a tutorial document for a hands-on learning experience with Synapse Data Flows.

Data cleansing and data prep with synapse data flowsMark Kromer

Mapping Data Flows Training April 2021Mark Kromer

Mapping Data Flows Perf Tuning April 2021Mark Kromer

This document discusses optimizing performance for data flows in Azure Data Factory. It provides sample timing results for various scenarios and recommends settings to improve performance. Some best practices include using memory optimized Azure integration runtimes, maintaining current partitioning, scaling virtual cores, and optimizing transformations and sources/sinks. The document also covers monitoring flows to identify bottlenecks and global settings that affect performance.

Data Lake ETL in the Cloud with ADFMark Kromer

This document discusses using Azure Data Factory (ADF) for data lake ETL processes in the cloud. It describes how ADF can ingest data from on-premises, cloud, and SaaS sources into a data lake for preparation, transformation, enrichment, and serving to downstream analytics or machine learning processes. The document also provides several links to YouTube videos and articles about using ADF for these tasks.

Azure Data Factory Data Wrangling with Power QueryMark Kromer

Azure Data Factory now allows users to perform data wrangling tasks through Power Query activities, translating M scripts into ADF data flow scripts executed on Apache Spark. This enables code-free data exploration, preparation, and operationalization of Power Query workflows within ADF pipelines. Examples of use cases include data engineers building ETL processes or analysts operationalizing existing queries to prepare data for modeling, with the goal of providing a data-first approach to building data flows and pipelines in ADF.

Azure Data Factory Data Flow Performance Tuning 101Mark Kromer

The document provides performance timing results and recommendations for optimizing Azure Data Factory data flows. Sample 1 processed a 421MB file with 887k rows in 4 minutes using default partitioning on an 80-core Azure IR. Sample 2 processed a table with the same size and transforms in 3 minutes using source and derived column partitioning. Sample 3 processed the same size file in 2 minutes with default partitioning. The document recommends partitioning strategies, using memory optimized clusters, and scaling cores to improve performance.

Data Quality Patterns in the Cloud with ADFMark Kromer

Azure Data Factory Data Flows Training (Sept 2020 Update)Mark Kromer

Mapping data flows allow for code-free data transformation using an intuitive visual interface. They provide resilient data flows that can handle structured and unstructured data using an Apache Spark engine. Mapping data flows can be used for common tasks like data cleansing, validation, aggregation, and fact loading into a data warehouse. They allow transforming data at scale through an expressive language without needing to know Spark, Scala, Python, or manage clusters.

Data quality patterns in the cloud with ADFMark Kromer

Azure Data Factory can be used to build modern data warehouse patterns with Azure SQL Data Warehouse. It allows extracting and transforming relational data from databases and loading it into Azure SQL Data Warehouse tables optimized for analytics. Data flows in Azure Data Factory can also clean and join disparate data from Azure Storage, Data Lake Store, and other data sources for loading into the data warehouse. This provides simple and productive ETL capabilities in the cloud at any scale.

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.

Data Quality Patterns in the Cloud with Azure Data FactoryMark Kromer

ADF Mapping Data Flows Level 300Mark Kromer

Azure Data Factory can now use Mapping Data Flows to orchestrate ETL workloads. Mapping Data Flows allow users to visually design transformations on data from disparate sources and load the results into Azure SQL Data Warehouse for analytics. The key benefits of Mapping Data Flows are that they provide a visual interface for building expressions to cleanse and join data with auto-complete assistance and live previews of expression results.

ADF Mapping Data Flows Training V2Mark Kromer

ADF Mapping Data Flows Training Slides V1Mark Kromer

Mapping Data Flow is a new feature of Azure Data Factory that allows users to build data transformations in a visual interface without code. It provides a serverless, scale-out transformation engine to transform data at scale in the cloud in a resilient manner for big data scenarios involving unstructured data. Mapping Data Flows can be operationalized with Azure Data Factory's scheduling, control flow, and monitoring capabilities.

ADF Mapping Data Flow Private Preview MigrationMark Kromer

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.

Fabric Data Factory Pipeline Copy Perf Tips.pptxMark Kromer

Build data quality rules and data cleansing into your data pipelinesMark Kromer

Mapping Data Flows Training deck Q1 CY22Mark Kromer

Data cleansing and prep with synapse data flowsMark Kromer

Data cleansing and data prep with synapse data flowsMark Kromer

Mapping Data Flows Training April 2021Mark Kromer

Mapping Data Flows Perf Tuning April 2021Mark Kromer

Data Lake ETL in the Cloud with ADFMark Kromer

Azure Data Factory Data Wrangling with Power QueryMark Kromer

Azure Data Factory Data Flow Performance Tuning 101Mark Kromer

Data Quality Patterns in the Cloud with ADFMark Kromer

Azure Data Factory Data Flows Training (Sept 2020 Update)Mark Kromer

Data quality patterns in the cloud with ADFMark Kromer

Azure Data Factory Data Flows Training v005Mark Kromer

Data Quality Patterns in the Cloud with Azure Data FactoryMark Kromer

ADF Mapping Data Flows Level 300Mark Kromer

ADF Mapping Data Flows Training V2Mark Kromer

ADF Mapping Data Flows Training Slides V1Mark Kromer

ADF Mapping Data Flow Private Preview MigrationMark Kromer

Azure Data Factory ETL Patterns in the CloudMark Kromer

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Big Data in the Cloud with Azure Marketplace Images

1. ModernDataWarehouse • Hadoop as an ETL/ELT optimization engine • Integration of streaming engines for data ingestion • Lambda and Hybrid architectures that include multiple processing and storage layers DataLakes • A good starting point for an organization to enter into Big Data • Collect data points as files in a massively distributed file system for analysis, processing and warehousing AdvancedAnalytics • Real-time stream- based analytics • Machine Learning • Leverage statistics- based packages in R and Python for exploratory analysis • Uncover the valuable data points from Data Lake

2. Scale • Elasticity. Don’t engineer for worst- case scenario all the time. • Moving data to Azure means taking advantage of Microsoft’s bottomless storage and enormous compute resources. SpeedofDeployment • Customers want to take advantage of Azure’s ability to automate deployments • A huge advantage of Cloud Data solutions in Azure is to quickly spin-up & spin- down environments in dev/test, QA, production as well as pause & stop PolyglotEnvironments • Azure Data solutions are able to incorporate complex heterogeneous data environments that include a large spectrum of engine and storage options from RDBMS to NoSQL to Hadoop • Take advantage of the Azure Marketplace

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