Building Big Data Applications using Spark, Hive, HBase and Kafka

Editor's Notes

• #8: Azure HDInsight is a secure and managed platform for building data lakes on Azure based on the Apache Hadoop and Spark frameworks. So, what all does HDInsight have to offer? Reliable Open Source analytics with an Industry leading SLAHDInsight allows you to easily spin up open source cluster types guaranteed with the industry’s best 99.9% SLA and 24/7 support. We guarantee this SLA for the entire big data solution, not just the VM instances. HDInsight is architected for full redundancy and high availability including head node replication, data geo-replication, and built-in standby NameNode making HDInsight resilient to critical failures not addressed in standard Hadoop implementations. Azure also offers cluster monitoring and 24x7 enterprise support backed by Microsoft and Hortonworks with 37 combined committers for Hadoop core, more than all other managed cloud providers combined to support your deployment and the ability to fix and commit code back to Hadoop. Enterprise Grade Security & MonitoringHDInsight protects your data assets and easily extends your on-premise security and governance controls to the cloud. We feature single sign-on (SSO), multi-factor authentication and seamless management of millions of identities through Azure Active Directory. You can authorize users and groups with fine-grained access control policies over all your enterprise data with Apache Ranger. HDInsight meets HIPAA, PCI, SOC compliance, ensuring your enterprise data assets are always protected with the highest security and regulatory compliance. To ensure the highest level of business continuity, HDInsight extends capabilities for alerting, monitoring, defining pre-emptive actions, and enhanced workload protection through native integration with Azure Operations Management Suite (OMS). Most Productive platform for developers and scientists HDInsight offers developers tailored experiences through rich productivity suites for Hadoop & Spark with integrated development environments using Visual Studio, Eclipse, and IntelliJ supporting Scala, Python, R, Java, and .Net. HDInsight gives data scientists the ability to create narratives that combine code, statistical equations, and visualizations that tell a story about the data through integration to the two most popular notebooks: Jupyter and Zeppelin. HDInsight is also the only managed cloud Hadoop solution with integration to Microsoft R Server. Multi-threaded math libraries and transparent parallelization in R Server means handling up to 1000x more data and up to 50x faster speeds than open source R—helping you train more accurate models for better predictions than previously possible. Cost effective cloud scaleHDInsight has decoupled compute and storage, enabling you to cost-effectively scale workloads up or down, independent of storage. Local storage can still be used for caching and fast I/O. Spark and interactive Hive users can choose SSD memory for interactive performance; while Kafka users can retain all streaming data in premium managed disks. You only pay for the compute and storage you use and are given the ability to choose any Azure VM types that enables the best utilization of resources. A recent study showed HDInsight delivering 63% lower TCO than deploying Hadoop on premises over 5 years.* Integration with leading Productivity ApplicationsIn the broader ecosystem for Hadoop, there is a thriving market of independent software vendors (ISVs) who provide value added solutions. Through a unique design where every cluster is extended with edge nodes and script action, HDInsight lets customers spin up Hadoop and Spark clusters pre-integrated and pre-tuned with any ISV application out-of-the-box. Datameer, Cask, AtScale, StreamSets are few such applications, which are very popular on the HDInsight platform today. Easy for administrators to manageWith HDInsight, administrators can deploy Hadoop in the cloud without buying new hardware or incurring other up-front costs. There’s also no time-consuming installation or set up. There is also no need to patch the operating system or upgrade the Hadoop versions. Azure does it for you. Launch your first cluster in minutes.
• #15: The new world of HDInsight 4.0 with Hadoop 3.0, brings the Spark and Hive worlds closer together. Lets see, how… Before Hadoop 3.0, the Spark executors would directly access the Hive metastore. While, on the surface, this seems like a fine thing to do, it is rife with problems. The new architecture instead requires explicit registration of Hive transactional tables as Spark external tables through Hive Warehouse Connector. While it adds one extra step during configuration, this approach greatly increases the reliability of data access. Hive Warehouse Connector supports efficient predicate pushdown and Apache Arrow-based communication between Spark executors and Hive LLAP daemons. This results in overall small overhead of communication between two systems. With Hive Warehouse Connector, Apache Spark on HDInsight 4.0 gets mature transactional capabilities.​ The new integration between Apache Spark and Hive LLAP in HDInsight 4.0 delivers new capabilities for business analysts, data scientists, and data engineers. Business analysts get a performant SQL engine in the form of Hive LLAP (Interactive Query) while data scientists and data engineers get a great platform for ML experimentation and ETL with Apache Spark over transactional data in Hive tables.​
• #16: Reference https://azure.microsoft.com/en-us/blog/deploying-apache-airflow-in-azure-to-build-and-run-data-pipelines/
• #18: Build 2015
• #20: Transfer data over network with TLS Over internet - You can transfer data to Azure storage over a regular internet connection using any one of several tools such as: Azure Storage Explorer, AzCopy, Azure Powershell, and Azure CLI. See Moving data to and from Azure Storage for more information. Express Route - ExpressRoute is an Azure service that lets you create private connections between Microsoft datacenters and infrastructure that’s on your premises or in a colocation facility. ExpressRoute connections do not go over the public Internet, and offer higher security, reliability, and speeds with lower latencies than typical connections over the Internet. For more information, see Create and modify an ExpressRoute circuit. Data Box online data transfer - Data Box Edge and Data Box Gateway are online data transfer products that act as network storage gateways to manage data between your site and Azure. Data Box Edge, an on-premises network device, transfers data to and from Azure and uses artificial intelligence (AI)-enabled edge compute to process data. Data Box Gateway is a virtual appliance with storage gateway capabilities. For more information, see Azure Data Box Documentation - Online Transfer. Shipping data Offline Import / Export service - you can send physical disks to Azure and they will be uploaded for you. For more information, see What is Azure Import/Export service?. Data Box offline data transfer - Data Box, Data Box Disk, and Data Box Heavy devices help you transfer large amounts of data to Azure when the network isn’t an option. These offline data transfer devices are shipped between your organization and the Azure datacenter. They use AES encryption to help protect your data in transit, and they undergo a thorough post-upload sanitization process to delete your data from the device. For more information, see Azure Data Box Documentation - Offline Transfer.
• #23: Azure Data Lake Storage Gen2 is Azure’s Storage platform for high performance analytics It is built on the strong foundation of Blob Storage which is Azure’s object storage platform and has been serving customers and various use cases (including some analytics use cases) for over a decade ADLS Gen2 is designed with native file system semantics and optimized for high performance analytics (example: rename folder operations which is very common in spark workloads is a single metadata operation as opposed to a large number for individual object operations) ADLS Gen2 also supports POSIX ACL’s which is an open source industry standard ADLS Gen2 currently only supports a small subset of the Blob capabilities (authentication and redundancy) but several of the other Blob capabilities will light up once we support “interoperability” – which is the ability to run multiple protocols on the same account. This is planned to roll out in waves throughout the calendar year Blob interoperability also lights up integrations such as ASA, Event Hubs etc since this was previously done for Blob storage. This includes SDK’s as the Blob SDK’s can be used on the account as opposed to writing brand new SDK’s. ADLS Gen2 is GA and available in all Azure regions and is the recommended storage platform for analytics pipelines
• #45: Before I describe specific capabilities and value propositions of HDInsight, let us take a quick look at the architecture of a HDInsight cluster. We will build upon this when we talk about security later on in the presentation. First off, a key difference between an on-premise Hadoop cluster and a HDInsight cluster is that with HDInsight, the storage and compute layers are separated. This allows for storage and compute to be scaled independently of each other. We have seen in numerous customer cases, that trying to combine storage and compute on to a single cluster often leads to underutilization of one or the other or both. With HDInsight, you can keep loading data in to Azure Storage Gen1 or Gen2 or in WASB. And you can create small or large clusters as and when needed. Each HDInsight cluster comes with 2 gateway nodes, 2 head nodes and 3 ZooKeeper nodes. In most cases, these are free of charge. As we will discuss later, we provision multiple of these nodes to ensure high availability. Each HDInsight cluster lives within a VNET. The gateway nodes are the ONLY public endpoints accessible from outside the VNET. As we will see later, this architecture allows you to securely lock down your HDInsight cluster.