ORLANDO, Fla., Sept. 14, 2018 — Earlier today, Microsoft Corporation announced its continuing support and commitment to enterprises seeking to use Hadoop for open source big data analytics in the cloud. Leading off the series of major upgrades to the Azure HDInsight service is the preview release of Hadoop 3.0, the transformational update to the Hadoop stack that enterprises have been waiting for since earlier this year. In addition, enterprises with strict security and compliance requirements will be able to secure their Azure HDInsight clusters using Enterprise Security Package. And there is something in this release for everybody! Spark developers will particularly like the series of innovations from Microsoft that will now allow them to quickly identify and resolve performance bottlenecks in their code.
“We have been honored to be part of the open source analytics community,” said Ryan Waite, Director of Big Data Product Management. “We’re making open source analytics central to our product strategy, from our investments in HDInsight, to our participation in projects like YARN, to our shift to using open source analytics in our internal data lake. The rate of innovation in this space is only increasing with Hadoop 3.0. We are excited to be able to bring this to our customers so that they too can accelerate their big data journey.”
Preview of Hadoop 3.0 in Azure HDInsight 4.0
First released back in December 2017, Hadoop 3.0 represents over 5 years of work across the community since the last major update to the Hadoop stack. Enterprises can now realize their data lake vision while efficiently incorporating deep learning frameworks in to their applications all on the same Hadoop stack that they are comfortable with.
Some of the key enhancements include:
With ACID semantics enabled by default, Hive 3.0 becomes more like a traditional database, making it easier for customers to build LOB applications on top of very large data sets.
- Apache Druid is an open source data store with indexing/caching capabilities on top of a column-oriented storage layout. With Apache Hive & Druid (now available by default), customers can do near real time exploratory analytics on incoming data.
- With Tensorflow, available by default, and GPU support, Hadoop 3.0 squarely targets the machine learning and deep learning scenarios.
- Azure is the first major cloud provider to offer managed Hadoop 3.0. This will enable Azure customers to start building new applications or update their existing applications to work with the new Hadoop 3.0 platform.
Enhanced enterprise grade security
Enterprise grade security and compliance is a critical requirement from all customers building big data applications that store or process sensitive financial, business, personal, and healthcare data in the cloud.
With the general availability of the Enterprise Security Package (ESP) customers can now:
- Ensure that users authenticate to their HDInsight clusters using their corporate domain credentials.
- Ensure that users are subject to rich fine-grained access policies (authored and managed in Apache Ranger) as per their corporate data access policies.
- Ensure that all access to critical data are logged and available in Apache Ranger for subsequent audit or forensic analysis as needed.
In addition, enterprises using Apache Kafka will appreciate the greater defense in depth they can achieve through BYOK encryption for Apache Kafka.
Advanced Debugging Tools for HDInsight Spark developers
Developers, data scientists and analysts are already know that Azure HDInsight offers rich development and debugging capabilities in a tool of their choice; IntelliJ, Eclipse, VSCode, Jupyter and Zeppelin notebooks etc.
Microsoft has now stepped it up one more level! Debugging large distributed big data application running on hundreds of nodes is hard and time consuming. Microsoft is now bringing its decade long experience of running and debugging nearly billions of jobs to the open-source world of Apache Spark. Key enhancements include:
- Job graph with playback and heatmap identifying read/write bottlenecks.
- Job critical path analysis and visualization.
- Data skew detection and analysis.
- Job specific data management including data preview, download, and copy.
Availability of key ISV applications on Azure HDInsight
Azure HDInsight supports a vibrant application ecosystem with the most popular big data applications available on Azure Marketplace. Customers will now find three new applications that they can use with Azure HDInsight covering key areas such as data governance, SQL-friendly queries over big data and application migration to Azure:
- Starburst: Presto connectors on Azure HDInsight scales on demand and integrates other data sources with HDInsight.
- Waterline Data: A data cataloging and governance solution used by several Azure customers.
“We are very excited to be launching the Waterline Data Catalog on Microsoft Azure HDInsight, a valuable analytics service for the petabyte enterprises that are now moving their mountains of data into the cloud for dramatically faster and more cost-effective processing,” said Waterline Data CEO, Kailash Ambwani. “Our AI-driven, highly scalable Waterline Data Catalog extends the power of HDInsight by automating the classification and governance of data, rapidly rendering all of the organization’s data available for faster analytics and deeper insights. Together, Microsoft and Waterline Data are helping organizations push the power of their data to new heights, enabling everything from real-time IoT services to cutting-edge AI and machine learning-based applications for greater innovation and competitiveness in today’s Data Economy.”
About Azure HDInsight
Azure HDInsight is a fully managed cluster service that enables customers to process and gain insights from massive amounts of data using Hadoop, Spark, Hive, HBase, Kafka, Storm, and distributed R. The service is available in 27 public regions and Azure Government Clouds in the US and Germany.
Azure HDInsight powers some of the top customer’s mission critical applications ranging in a wide variety of sectors including, manufacturing, retail education, nonprofit, government, healthcare, media, banking, telecommunication, insurance, and many more industries ranging in use cases from ETL to Data Warehousing, from Machine Learning to IoT, and many more.