Here are the slides for my talk "An intro to Azure Data Lake" at Techorama NL 2018. The session was held on Tuesday October 2nd from 15:00 - 16:00 in room 7.
Modernizing ETL with Azure Data Lake: Hyperscale, multi-format, multi-platfor...Michael Rys
More and more customers who are looking to modernize analytics needs are exploring the data lake approach in Azure. Typically, they are most challenged by a bewildering array of poorly integrated technologies and a variety of data formats, data types not all of which are conveniently handled by existing ETL technologies. In this session, we’ll explore the basic shape of a modern ETL pipeline through the lens of Azure Data Lake. We will explore how this pipeline can scale from one to thousands of nodes at a moment’s notice to respond to business needs, how its extensibility model allows pipelines to simultaneously integrate procedural code written in .NET languages or even Python and R, how that same extensibility model allows pipelines to deal with a variety of formats such as CSV, XML, JSON, Images, or any enterprise-specific document format, and finally explore how the next generation of ETL scenarios are enabled though the integration of Intelligence in the data layer in the form of built-in Cognitive capabilities.
U-SQL combines SQL and C# to allow for querying and analyzing large amounts of structured and unstructured data stored in Azure Data Lake Store. U-SQL queries can access data across various Azure data services and provide analytics capabilities like window functions and ranking functions. The language also allows for extensibility through user-defined functions, aggregates, and operators written in C#. U-SQL queries are compiled and executed on Azure Data Lake Analytics, which provides a scalable analytics service based on Apache YARN.
This presentation focuses on the value proposition for Azure Databricks for Data Science. First, the talk includes an overview of the merits of Azure Databricks and Spark. Second, the talk includes demos of data science on Azure Databricks. Finally, the presentation includes some ideas for data science production.
Azure Data Lake Analytics provides a big data analytics service for processing large amounts of data stored in Azure Data Lake Store. It allows users to run analytics jobs using U-SQL, a language that unifies SQL with C# for querying structured, semi-structured and unstructured data. Jobs are compiled, scheduled and run in parallel across multiple Azure Data Lake Analytics Units (ADLAUs). The key components include storage, a job queue, parallelization, and a U-SQL runtime. Partitioning input data improves performance by enabling partition elimination and parallel aggregation of query results.
This document provides an overview of Azure Databricks, including:
- Azure Databricks is an Apache Spark-based analytics platform optimized for Microsoft Azure cloud services. It includes Spark SQL, streaming, machine learning libraries, and integrates fully with Azure services.
- Clusters in Azure Databricks provide a unified platform for various analytics use cases. The workspace stores notebooks, libraries, dashboards, and folders. Notebooks provide a code environment with visualizations. Jobs and alerts can run and notify on notebooks.
- The Databricks File System (DBFS) stores files in Azure Blob storage in a distributed file system accessible from notebooks. Business intelligence tools can connect to Databricks clusters via JDBC
Spark SQL is a module for structured data processing on Spark. It integrates relational processing with Spark's functional programming API and allows SQL queries to be executed over data sources via the Spark execution engine. Spark SQL includes components like a SQL parser, a Catalyst optimizer, and Spark execution engines for queries. It supports HiveQL queries, SQL queries, and APIs in Scala, Java, and Python.
Analyzing StackExchange data with Azure Data LakeBizTalk360
Big data is the new big thing where storing the data is the easy part. Gaining insights in your pile of data is something different. Based on a data dump of the well-known StackExchange websites, we will store & analyse 150+ GB of data with Azure Data Lake Store & Analytics to gain some insights about their users. After that we will use Power BI to give an at a glance overview of our learnings.
If you are a developer that is interested in big data, this is your time to shine! We will use our existing SQL & C# skills to analyse everything without having to worry about running clusters.
Building a data lake is a daunting task. The promise of a virtual data lake is to provide the advantages of a data lake without consolidating all data into a single repository. With Apache Arrow and Dremio, companies can, for the first time, build virtual data lakes that provide full access to data no matter where it is stored and no matter what size it is.
Best Practices and Performance Tuning of U-SQL in Azure Data Lake (SQL Konfer...Michael Rys
The document discusses best practices and performance tuning for U-SQL in Azure Data Lake. It provides an overview of U-SQL query execution, including the job scheduler, query compilation process, and vertex execution model. The document also covers techniques for analyzing and optimizing U-SQL job performance, including analyzing the critical path, using heat maps, optimizing AU usage, addressing data skew, and query tuning techniques like data loading tips, partitioning, predicate pushing and column pruning.
Building an ETL pipeline for Elasticsearch using SparkItai Yaffe
How we, at eXelate, built an ETL pipeline for Elasticsearch using Spark, including :
* Processing the data using Spark.
* Indexing the processed data directly into Elasticsearch using elasticsearch-hadoop plugin-in for Spark.
* Managing the flow using some of the services provided by AWS (EMR, Data Pipeline, etc.).
The presentation includes some tips and discusses some of the pitfalls we encountered while setting-up this process.
Cortana Analytics Workshop: Azure Data LakeMSAdvAnalytics
Rajesh Dadhia. This session introduces the newest services in the Cortana Analytics family. Azure Data Lake is a hyper-scale data repository designed for big data analytics workloads. It provides a single place to store any type of data in its native format. In this session, we will show how the HDFS compatibility of Azure Data Lake as a Hadoop File System enables all Hadoop workloads including Azure HDInsight, Hortonworks and Cloudera. Further, we will focus on the key capabilities of the Azure Data Lake that make it an ideal choice for storing, accessing and sharing data for a wide range of analytics applications. Go to https://channel9.msdn.com/ to find the recording of this session.
What's new in Mondrian 4? Slides for a talk given by Julian Hyde to the Pentaho Bay Area User Group meetup in San Francisco on April 3rd, 2014.
Topics covered include attributes and attribute hierarchies, measure groups and aggregate tables, physical schema, and how to download and start using Mondrian 4 beta with Pentaho CE.
Spark as a Service with Azure DatabricksLace Lofranco
Presented at: Global Azure Bootcamp (Melbourne)
Participants will get a deep dive into one of Azure’s newest offering: Azure Databricks, a fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure. In this session, we will go through Azure Databricks key collaboration features, cluster management, and tight data integration with Azure data sources. We’ll also walk through an end-to-end Recommendation System Data Pipeline built using Spark on Azure Databricks.
Azure Data Lake and Azure Data Lake AnalyticsWaqas Idrees
This document provides an overview and introduction to Azure Data Lake Analytics. It begins with defining big data and its characteristics. It then discusses the history and origins of Azure Data Lake in addressing massive data needs. Key components of Azure Data Lake are introduced, including Azure Data Lake Store for storing vast amounts of data and Azure Data Lake Analytics for performing analytics. U-SQL is covered as the query language for Azure Data Lake Analytics. The document also touches on related Azure services like Azure Data Factory for data movement. Overall it aims to give attendees an understanding of Azure Data Lake and how it can be used to store and analyze large, diverse datasets.
Azure Data Factory is one of the newer data services in Microsoft Azure and is part of the Cortana Analyics Suite, providing data orchestration and movement capabilities.
This session will describe the key components of Azure Data Factory and take a look at how you create data transformation and movement activities using the online tooling. Additionally, the new tooling that shipped with the recently updated Azure SDK 2.8 will be shown in order to provide a quickstart for your cloud ETL projects.
Microsoft Ignite AU 2017 - Orchestrating Big Data Pipelines with Azure Data F...Lace Lofranco
Data orchestration is the lifeblood of any successful data analytics solution. Take a deep dive into Azure Data Factory's data movement and transformation activities, particularly its integration with Azure's Big Data PaaS offerings such as HDInsight, SQL Data warehouse, Data Lake, and AzureML. Participants will learn how to design, build and manage big data orchestration pipelines using Azure Data Factory and how it stacks up against similar Big Data orchestration tools such as Apache Oozie.
Video of presentation:
https://channel9.msdn.com/Events/Ignite/Australia-2017/DA332
Bringing the Power and Familiarity of .NET, C# and F# to Big Data Processing ...Michael Rys
This document introduces .NET for Apache Spark, which allows .NET developers to use the Apache Spark analytics engine for big data and machine learning. It discusses why .NET support is needed for Apache Spark given that much business logic is written in .NET. It provides an overview of .NET for Apache Spark's capabilities including Spark DataFrames, machine learning, and performance that is on par or faster than PySpark. Examples and demos are shown. Future plans are discussed to improve the tooling, expand programming experiences, and provide out-of-box experiences on platforms like Azure HDInsight and Azure Databricks. Readers are encouraged to engage with the open source project and provide feedback.
Jupyter Notebooks and Apache Spark are first class citizens of the Data Science space, a truly requirement for the "modern" data scientist. Now with Azure Synapse these two computing powers are available to the .NET Developer. And .NET is available for all data scientists. Let's look what .net can do for notebooks and spark inside Azure Synapse and what are Synapse, notebooks and spark.
This document discusses optimizing Apache Spark (PySpark) workloads in production. It provides an agenda for a presentation on various Spark topics including the primary data structures (RDD, DataFrame, Dataset), executors, cores, containers, stages and jobs. It also discusses strategies for optimizing joins, parallel reads from databases, bulk loading data, and scheduling Spark workflows with Apache Airflow. The presentation is given by a solution architect from Accionlabs, a global technology services firm focused on emerging technologies like Apache Spark, machine learning, and cloud technologies.
Building iot applications with Apache Spark and Apache BahirLuciano Resende
We leave in a connected world where connected devices are becoming part of our day to day and are providing invaluable streams of data. In this talk, we will introduce you to Apache Bahir and some of its IoT connectors available for Apache Spark. We will also go over the details on how to build, test and deploy an IoT application for Apache Spark using the MQTT data source for the new Apache Spark Structure Streaming functionality.
Writing Apache Spark and Apache Flink Applications Using Apache BahirLuciano Resende
Big Data is all about being to access and process data in various formats, and from various sources. Apache Bahir provides extensions to distributed analytic platforms providing them access to different data sources. In this talk we will introduce you to Apache Bahir and its various connectors that are available for Apache Spark and Apache Flink. We will also go over the details of how to build, test and deploy an Spark Application using the MQTT data source for the new Apache Spark 2.0 Structure Streaming functionality.
Author: Stefan Papp, Data Architect at “The unbelievable Machine Company“. An overview of Big Data Processing engines with a focus on Apache Spark and Apache Flink, given at a Vienna Data Science Group meeting on 26 January 2017. Following questions are addressed:
• What are big data processing paradigms and how do Spark 1.x/Spark 2.x and Apache Flink solve them?
• When to use batch and when stream processing?
• What is a Lambda-Architecture and a Kappa Architecture?
• What are the best practices for your project?
IoT Applications and Patterns using Apache Spark & Apache BahirLuciano Resende
The Internet of Things (IoT) is all about connected devices that produce and exchange data, and building applications that produce insights from these high volumes of data is very challenging and require a understanding of multiple protocols, platforms and other components. On this session, we will start by providing a quick introduction to IoT, some of the common analytic patterns used on IoT, and also touch on the MQTT protocol and how it is used by IoT solutions some of the quality of services tradeoffs to be considered when building an IoT application. We will also discuss some of the Apache Spark platform components, the ones utilized by IoT applications to process devices streaming data.
We will also talk about Apache Bahir and some of its IoT connectors available for the Apache Spark platform. We will also go over the details on how to build, test and deploy an IoT application for Apache Spark using the MQTT data source for the new Apache Spark Structure Streaming functionality.
What Is Apache Spark? | Introduction To Apache Spark | Apache Spark Tutorial ...Simplilearn
This presentation about Apache Spark covers all the basics that a beginner needs to know to get started with Spark. It covers the history of Apache Spark, what is Spark, the difference between Hadoop and Spark. You will learn the different components in Spark, and how Spark works with the help of architecture. You will understand the different cluster managers on which Spark can run. Finally, you will see the various applications of Spark and a use case on Conviva. Now, let's get started with what is Apache Spark.
Below topics are explained in this Spark presentation:
1. History of Spark
2. What is Spark
3. Hadoop vs Spark
4. Components of Apache Spark
5. Spark architecture
6. Applications of Spark
7. Spark usecase
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Who should take this Scala course?
1. Professionals aspiring for a career in the field of real-time big data analytics
2. Analytics professionals
3. Research professionals
4. IT developers and testers
5. Data scientists
6. BI and reporting professionals
7. Students who wish to gain a thorough understanding of Apache Spark
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Alpine academy apache spark series #1 introduction to cluster computing wit...Holden Karau
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Big Data Web applications for Interactive Hadoop by ENRICO BERTI at Big Data...Big Data Spain
This talk describes how open source Hue [1] was built in order to provide a better Hadoop User Experience. The underlying technical details of its architecture, the lessons learned and how it integrates with Impala, Search and Spark under the cover will be explained.
Spark Streaming @ Berlin Apache Spark Meetup, March 2015Stratio
Spark Streaming allows real-time processing of live data streams using the Spark engine. It discretizes streams into batches represented as RDDs, on which transformations like maps, filters and reductions can be applied. Receivers bring in data from sources like Kafka, Flume and files. Windows allow aggregating data over time periods like counting words in the last 60 seconds every 10 seconds. Combined with Spark's machine learning and graph processing libraries, it enables applications like Twitter sentiment analysis.
Planning with Polyalgebra: Bringing Together Relational, Complex and Machine ...Julian Hyde
A talk from given by Julian Hyde and Tomer Shiran at Hadoop Summit, Dublin.
Data scientists and analysts want the best API, DSL or query language possible, not to be limited by what the processing engine can support. Polyalgebra is an extension to relational algebra that separates the user language from the engine, so you can choose the best language and engine for the job. It also allows the system to optimize queries and cache results. We demonstrate how Ibis uses Polyalgebra to execute the same Python-based machine learning queries on Impala, Drill and Spark. And we show how to build Polyalgebra expressions in Calcite and how to define optimization rules and storage handlers.
The document discusses polyalgebra, an extended form of relational algebra that can handle complex data types like nested records and streaming data. It allows various data processing engines and SQL query engines to operate over different data sources using a single optimization framework. The document outlines the ecosystem of data stores, engines, and frameworks that can be used with polyalgebra and Calcite's rule-based query planning system. It provides examples of how relational algebra expressions capture the logic of SQL queries and how rules are used to optimize query plans.
Transformation Processing Smackdown; Spark vs Hive vs PigLester Martin
This document provides an overview and comparison of different data transformation frameworks including Apache Pig, Apache Hive, and Apache Spark. It discusses features such as file formats, source to target mappings, data quality checks, and core processing functionality. The document contains code examples demonstrating how to perform common ETL tasks in each framework using delimited, XML, JSON, and other file formats. It also covers topics like numeric validation, data mapping, and performance. The overall purpose is to help users understand the different options for large-scale data processing in Hadoop.
This document discusses Apache Spark, an open-source cluster computing framework. It provides an overview of Spark, including its main concepts like RDDs (Resilient Distributed Datasets) and transformations. Spark is presented as a faster alternative to Hadoop for iterative jobs and machine learning through its ability to keep data in-memory. Example code is shown for Spark's programming model in Scala and Python. The document concludes that Spark offers a rich API to make data analytics fast, achieving speedups of up to 100x over Hadoop in real applications.
The Nitty Gritty of Advanced Analytics Using Apache Spark in PythonMiklos Christine
Apache Spark is the next big data processing tool for Data Scientist. As seen on the recent StackOverflow analysis, it's the hottest big data technology on their site! In this talk, I'll use the PySpark interface to leverage the speed and performance of Apache Spark. I'll focus on the end to end workflow for getting data into a distributed platform, and leverage Spark to process the data for advanced analytics. I'll discuss the popular Spark APIs used for data preparation, SQL analysis, and ML algorithms. I'll explain the performance differences between Scala and Python, and how Spark has bridged the gap in performance. I'll focus on PySpark as the interface to the platform, and walk through a demo to showcase the APIs.
Talk Overview:
Spark's Architecture. What's out now and what's in Spark 2.0Spark APIs: Most common APIs used by Spark Common misconceptions and proper techniques for using Spark.
Demo:
Walk through ETL of the Reddit dataset. SparkSQL Analytics + Visualizations of the Dataset using MatplotLibSentiment Analysis on Reddit Comments
Apache Arrow at DataEngConf Barcelona 2018Wes McKinney
Wes McKinney is a leading open source developer who created Python's pandas library and now leads the Apache Arrow project. Apache Arrow is an open standard for in-memory analytics that aims to improve data sharing and reuse across systems by defining a common columnar data format and memory layout. It allows data to be accessed and algorithms to be reused across different programming languages with near-zero data copying. Arrow is being integrated into various data systems and is working to expand its computational libraries and language support.
Software Development Automation With Scripting LanguagesIonela
The Scripting languages are deployed in many operative systems, either in UNIX/Linux or Windows. These languages are developed for general purpose process automation and web programming. But you can consider using them for the software development process in many ways. Among these languages, awk and Perl are suitable for automate and speed up software development for embedded systems, because many embedded systems only have cross tool chain, without powerful IDE supports for process automation.
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2. Software installation(for computer, and tablet or mobile devices)
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Big Data Processing with Spark and .NET - Microsoft Ignite 2019
3. Apache Spark is an OSS fast analytics engine for big data and machine
learning
Improves efficiency through:
General computation graphs beyond map/reduce
In-memory computing primitives
Allows developers to scale out their user code & write in their language of
choice
Rich APIs in Java, Scala, Python, R, SparkSQL etc.
Batch processing, streaming and interactive shell
Available on Azure via
Azure Synapse Azure Databricks
Azure HDInsight IaaS/Kubernetes
4. .NET Developers 💖 Apache Spark…
A lot of big data-usable business logic (millions
of lines of code) is written in .NET!
Expensive and difficult to translate into
Python/Scala/Java!
Locked out from big data processing due to
lack of .NET support in OSS big data solutions
In a recently conducted .NET Developer survey (> 1000 developers), more than 70%
expressed interest in Apache Spark!
Would like to tap into OSS eco-system for: Code libraries, support, hiring
5. Goal: .NET for Apache Spark is aimed at providing
.NET developers a first-class experience when
working with Apache Spark.
Non-Goal: Converting existing Scala/Python/Java
Spark developers.
6. We are developing it in the open!
Contributions to foundational OSS projects:
• Apache Spark Core: SPARK-28271, SPARK-28278, SPARK-28283, SPARK-28282, SPARK-28284,
SPARK-28319, SPARK-28238, SPARK-28856, SPARK-28970, SPARK-29279, SPARK-29373
• Apache Arrow: ARROW-4997, ARROW-5019, ARROW-4839, ARROW-4502, ARROW-4737,
ARROW-4543, ARROW-4435, ARROW-4503, ARROW-4717, ARROW-4337, ARROW-5887,
ARROW-5908, ARROW-6314, ARROW-6682
• Pyrolite (Pickling Library): Improve pickling/unpickling performance, Add a Strong Name to
Pyrolite, Improve Pickling Performance, Hash set handling, Improve unpickling performance
.NET for Apache Spark is open source
• Website: https://dot.net/spark
• GitHub: https://github.com/dotnet/spark
• Version 0.6 released Oct 2019
Spark project improvement proposals:
• Interop support for Spark language extensions: SPARK-26257
• .NET bindings for Apache Spark: SPARK-27006
7. .NET provides full-spectrum Spark support
Spark DataFrames
with SparkSQL
Works with
Spark v2.3.x/v2.4.x
and includes
~300 SparkSQL
functions
Grouped Map
Delta Lake
.NET Spark UDFs
Batch &
streaming
Including
Spark Structured
Streaming and all
Spark-supported data
sources
.NET Standard 2.0
Works with
.NET Framework v4.6.1+
and .NET Core v2.1/v3.x
and includes C#/F#
support
.NET
Standard
Data Science
Including access to
ML.NET
Interactive Notebook
with C# REPL
Speed &
productivity
Performance optimized
interop, as fast or faster
than pySpark,
Support for HW
Vectorization
https://github.com/dotnet/spark/examples
8. 0.6
8
DataStreamWriter.PartitionBy()
RelationalGroupedDataset.Mean(),Max(),Avg(),Min(),Agg(),Count()
SparkSession.*Session(),Range(),Conf()
UDF with Row as a parameter
Delta Lake’s DeltaTable
SparkSession.Catalog
UDF with Array.Map as a return type
UDF debugging
Vector & GroupedMap UDFspark.yarn.archives support
Compatibility check for Microsoft.Spark.Worker
AssemblyLoader enhancement for loading UDFs
Resolver signer fix
Arrow & Pickling perf improvement
Arcade build infrastructure
TPC-H update with Arrow
DataStreamWriter.Trigger
ComplexTypes.MapType
Support for Spark 2.3.*, Spark 2.4.[1/2/4]
Worker binaries for MacOS
UDF with dependent types
DataFrameReader.Load() Source link for Nuget packageSparkFile
.NET for Apache Spark
12. What is happening when you write .NET Spark code?
DataFrame
SparkSQL
.NET for
Apache
Spark
.NET
Program
Did you
define
a .NET
UDF?
Regular execution path
(no .NET runtime during execution)
Same Speed as with Scala Spark
Interop between Spark and .NET
Faster than with PySpark
No
Yes
Spark
operation tree
13. Works everywhere!
Cross platform
Cross Cloud
Windows Ubuntu
Azure & AWS
Databricks
macOS
AWS EMR
Spark
Azure HDI
Spark
Installed out of
the box
Azure
Synapse
Installation docs
on Github
14. More
programming
experiences in
.NET
(UDAF, UDT
support, multi-
language UDFs)
What’s next?
Spark data
connectors in
.NET
(e.g., Apache Kafka,
Azure Blob Store,
Azure Data Lake)
Tooling
experiences
(e.g., Jupyter, VS
Code, Visual
Studio, others?)
Idiomatic
experiences
for C# and F#
(LINQ, Type
Provider)
Go to https://github.com/dotnet/spark and let us know what is important to you!
Out-of-Box
Experiences
(Azure Synapse,
Azure HDInsight,
Azure Databricks,
Cosmos DB
Spark, SQL 2019
BDC, …)
15. Call to action: Engage, use & guide us!
Useful links:
• http://github.com/dotnet/spark
• https://www.nuget.org/packages/Microsoft.Spark
https://aka.ms/GoDotNetForSpark
• https://docs.microsoft.com/dotnet/spark
Website:
• https://dot.net/spark (Request a Demo!)
Starter Videos .NET for Apache Spark 101:
• Watch on YouTube
• Watch on Channel 9
Available out-of-box on
Azure Synapse & Azure HDInsight Spark
Running .NET for Spark anywhere—
https://aka.ms/InstallDotNetForSpark
You & .NET