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There have been a number of enhancements to Hadoop recently when it comes to fast interactive querying with such products as and which are being used over slower interactive querying options such as / and batch processing options such as (see ). This has led to a question I have started to see from customers: Do I still need a data warehouse or can I just put everything in a data lake and report off of that using Hive LLAP or Spark SQL? Which leads to the argument: “Is the data warehouse dead?” I think what is confusing is the argument should not be over whether the “data warehouse” is dead but clarified if the “traditional data warehouse” is dead, as the reasons that a “data warehouse” is needed are greater than ever (i.e. Integrate many sources of data, reduce reporting stress on production systems, data governance including cleaning and mastering and security, historical analysis, user-friendly data structure, minimize silos, single version of the truth, etc – see ).

And what is meant by a “traditional” data warehouse is usually referring to a relational data warehouse built using (if using Microsoft products) and when a data lake is mentioned it is usually one that is built in Hadoop using (ADLS) and (which has for Spark SQL and Hive LLAP that is also called Interactive Query). I think the ultimate question is: Can all the benefits of a traditional relational data warehouse be implemented inside of a Hadoop data lake with interactive querying via Hive LLAP or Spark SQL, or should I use both a data lake and a relational data warehouse in my big data solution? The short answer is you should use both. The rest of this post will dig into the reasons why. I touched on this ultimate question in a blog that is now over a few years old at so this is a good time to provide an update. I also touched on this topic in my blogs,, and and my presentation.

The main benefits I hear of a data lake-only approach: Don’t have to load data into another system and therefore manage schemas across different systems, data load times can be expensive, data freshness challenges, operational challenges of managing multiple systems, and cost. While these are valid benefits, I don’t feel they are enough to warrant not having a relational data warehouse in your solution. First lets talk about cost and dismiss the incorrect assumption that Hadoop is cheaper: Hadoop can be 3x cheaper for data refinement, but to build a data warehouse in Hadoop it can be 3x more expensive due to the cost of writing complex queries and analysis (based on a and my experiences). Understand that a “big data” solution does not mean just using Hadoop-related technologies, but could mean a combination of Hadoop and relational technologies and tools. Many clients will build their solution using just Microsoft products, while others use a combination of both Microsoft and open source (see ). Building a data warehouse solution on the cloud or migrating to the cloud is often the best idea (see ) and you can often migrate to the cloud without retooling technology and skills.

I have seen Hadoop adopters typically falling into two broad categories: those who see it as a platform for big data innovation, and those who dream of it providing the same capabilities as an enterprise data warehouse but at a cheaper cost. Big data innovators are thriving on the Hadoop platform especially when used in combination with relational database technologies, mining and refining data at volumes that were never before possible. However, most of those who expected Hadoop to replace their enterprise data warehouse have been greatly disappointed, and in response have been building complex architectures that typically do not end up meeting their business requirements. As far as reporting goes, whether to have users report off of a data lake or via a relational database and/or a cube is a balance between giving users data quickly and having them do the work to join, clean and master data (getting IT out-of-the-way) versus having IT make multiple copies of the data and cleaning, joining and mastering it to make it easier for users to report off of the data but dealing with the delay in waiting for IT to do all this. The risk in the first case is having users repeating the process to clean/join/master data and cleaning/joining/mastering it wrong and getting different answers to the same question. Display Serial Interface Lvds.

Another risk in the first case is slower performance because the data is not laid out efficiently. Most solutions incorporate both to allow power users or data scientists to access the data quickly via a data lake while allowing all the other users to access the data in a relational database or cube, making a reality (as most users would not have the skills to access data in a data lake properly or at all so a cube would be appropriate as it provides a semantic layer among other advantages to make report building very easy – see. Relational data warehouses continue to meet the information needs of users and continue to provide value. Many people use them, depend on them, and don’t want them to be replaced with a data lake. Data lakes offer a rich source of data for data scientists and self-service data consumers (“power users”) and serves analytics and big data needs well. But not all data and information workers want to become power users. The majority (at least 90%) continue to need well-integrated, systematically cleansed, easy to access relational data that includes a large body of time-variant history.

These people are best served with a data warehouse. I can’t stress enough if you need high data quality reports you need to apply the exact same transformations to the same data to produce that report no matter what your technical implementation is. If you call it a data lake or a data warehouse, or use an ETL tool or Python code, the development and maintenance effort is still there. You need to avoid falling into the old mistake that the data lake does not need data governance. It’s not a place with unicorns and fairies that will magically make all the data come out properly – a data lake is just a glorified file folder.

Here are some of the reasons why it is not a good idea to have a data lake in Hadoop as your data warehouse and forgo a relational data warehouse: • Hadoop does not provide for very fast query reads in all use cases. While Hadoop has come a long way in this area, Hive LLAP and Spark SQL have limits on what type of queries they support (i.e. ( and ) was announced at Microsoft Connect, and with this post I’ll try to explain its use case. At a high level, think of it as a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with Azure Blog Storage, Azure Data Lake Storage (ADLS), Azure SQL Data Warehouse (SQL DW), Cosmos DB, Azure Event Hub, Apache Kafka for HDInsight, and Power BI (see ). Think of it as an alternative to HDInsight (HDI) and Azure Data Lake Analytics (ADLA).

It differs from HDI in that HDI is a PaaS-like experience that allows working with many more OSS tools at a less expensive cost. Databricks advantage is it is a Software-as-a-Service-like experience (or Spark-as-a-service) that is easier to use, has native Azure AD integration (HDI security is via Apache Ranger and is Kerberos based), has and (like a pause/resume), has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark. Note that all clusters within the same workspace share data among all of those clusters. Also note with built-in integration to SQL DW it can write directly to SQL DW, as opposed to HDInsight which cannot and therefore more steps are required: when HDInsight processes data it must write it back to Blob Storage and then requires Azure Data Factory (ADF) to move the data from Blob Storage to SQL DW. It is in limited public preview now: More info.

Is a developer event from Nov 15-17, where plenty of announcements are made. Here is a summary of the data platform related: • Azure Databricks: In preview, this is a fast, easy, and collaborative Apache Spark based analytics platform optimized for Azure. It delivers one-click set up, streamlined workflows, and an interactive workspace all integrated with Azure SQL Data Warehouse, Azure Storage, Azure Cosmos DB, Azure Active Directory, and Power BI. • Azure Cosmos DB with Apache Cassandra API: In preview, this enables Cassandra developers to simply use the Cassandra API in Azure Cosmos DB and enjoy the benefits of Azure Cosmos DB with the familiarity of the Cassandra SDKs and tools, with no code changes to their application.. See • Microsoft joins the MariaDB Foundation: Microsoft is a platinum sponsor – MariaDB is a community of the MySQL relational database management system and Microsoft will be actively contributing to MariaDB and the MariaDB community. • Azure Database for MariaDB: An upcoming preview will bring fully managed service capabilities to MariaDB, further demonstrating Microsoft’s commitment to meeting customers and developers where they are by offering their favorite technologies on Azure.

• Azure SQL Database with Machine Learning Services: In preview this provides support for machine learning models inside Azure SQL Database. This makes it seamless for data scientists and developers to create and train models in Azure Machine Learning and deploy models directly to Azure SQL Database to create predictions at blazing fast speeds • Visual Studio Code Tools for AI: In preview, create, train, manage, and deploy AI models with all the productivity of Visual Studio and the power of Azure. Works on Windows and MacOS. Better late than never: The, which is a renaming of the, released an appliance update (AU6) about a year ago, and I missed the.

Below is what is new in this release, also called APS 2016. APS is alive and well and there will be another AU next calendar year: Microsoft is pleased to announce that the appliance update, Analytics Platform System (APS) 2016, has been released to manufacturing and is now generally available. APS is Microsoft’s scale-out Massively Parallel Processing fully integrated system for data warehouse specific workloads. This appliance update builds on the SQL Server 2016 release as a foundation to bring you many value-added features. APS 2016 offers additional language coverage to support migrations from SQL Server and other platforms.

It also features improved security for hybrid scenarios and the latest security and bug fixes through new firmware and driver updates. SQL Server 2016 APS 2016 runs on the latest SQL Server 2016 release and now uses the default database compatibility which can support improved query performance. SQL Server 2016 allows APS to offer features such as secondary index support for CCI tables and PolyBase Kerberos support.

Transact-SQL APS 2016 supports a broader set of T-SQL compatibility, including support for wider rows and a large number of rows,, and. For greater analysis flexibility, APS supports full window frame syntax for and additional windowing functions like,, and. Additional functions like and work with new data type support for and. For the full set of supported T-SQL, please visit the.

PolyBase/Hadoop enhancements PolyBase now supports the latest Hortonworks HDP 2.4 and HDP 2.5. This appliance update provides enhanced security through Kerberos support via database-scoped credentials and credential support with Azure Storage Blobs for added security across big data analysis. Install and upgrade enhancements Hardware architecture updates bring the latest generation processor support (Broadwell), DDR4 DIMMs, and improved DIMM throughput – these will ship with hardware purchased from HPE, Dell or Quanta. This update offers customers an enhanced upgrade and deployment experience on account of pre-packaging of certain Windows Server updates, hotfixes, and an installer that previously required an on-site download. APS 2016 also supports Fully Qualified Domain Name support, making it possible to setup a domain trust to the appliance. It also ships with the latest firmware/driver updates containing security updates and fixes. Flexibility of choice with Microsoft’s data warehouse portfolio The latest APS update is an addition to already existing data warehouse portfolio from Microsoft, covering a range of technology and deployment options that help customers get to insights faster.

Customers exploring data warehouse products can also consider or, a cloud based fully managed service. Next Steps For more details about these features, please visit our. Azure SQL Database Managed Instance is a new flavor of Azure SQL Database that is a game changer. It offers near-complete SQL Server compatibility and network isolation to easily lift and shift databases to Azure (you can literally backup an on-premise database and restore it into an Azure SQL Database Managed Instance).

Think of it as an enhancement to Azure SQL Database that is built on the same PaaS infrastructure and maintains all it’s features (i.e. Active geo-replication, high availability, automatic backups, database advisor, threat detection, intelligent insights, vulnerability assessment, etc) but adds support for databases up to 35TB, VNET, SQL Agent, cross-database querying, replication, etc.

So, you can migrate your databases from on-prem to Azure with very little migration effort which is a big improvement from the current Singleton or Elastic Pool flavors which can require substantial changes. I have created a presentation about Managed Instance. If you are not familiar with Azure SQL Database, first check out my. Azure SQL Database Managed Instance is in private preview, and will be in public preview this calendar year and it will be generally available next calendar year. For more details see the at Ignite by Drazen Sumic called “Modernize your on-premises applications with SQL Database Managed Instances (BRK2217)” and this by Lindsey Allen.

There was also a at Ignite called “What’s new with Azure SQL Database: Focus on your business, not on the database (BRK2230)” on the new features in SQL Database (Adaptive Query Processing, SQL Graph, Automatic Tuning, Intelligent Insights, Vulnerability Assessment, Service Endpoint) as well details on Azure Data Sync and an introduction to Managed Instances. There were so many good at, all of which can be viewed on-demand. I wanted to list the big data related presentations that I found the most useful. It’s a lot of stuff to watch and with our busy schedules can be quite challenging to view them all. What I do is set aside 40 minutes every day to watch half a session (they are 75 minutes).

If may take a few weeks, but if you consistently watch you will be rewarded by a much better understanding of all the product options and their uses cases, and my last blog post () can be used as a summary of all these options:: More and more customers who are looking to modernize their data centers have the need to lift and shift their fleet of databases to public cloud with the low effort and cost. We’ve developed Azure SQL Database to be the ideal destination, with enterprise security, full application compatibility and unique intelligent PaaS capabilities that reduce overall TCO.

In this session, through preview stories and demos learn what SQL Database Managed Instances are, and how you can use them to speed up and simplify your journey to cloud.: Have you ever wondered what’s the secret sauce that allows a company to use their data effectively? How do they ingest all their data, analyze it, and then make it available to thousands of end users? What happens if you need to scale the solution?

Come find out how some of the top companies in the world are building big data solutions with Azure Data Lake, Azure HDInsight, Azure SQL Data Warehouse, and Azure Analysis Services. We cover some of the reference architectures of these companies, best practices, and sample some of the new features that enable insight at the speed of thought.: Today’s organizations must adapt quickly to change, using new technologies to fuel competitive advantage, or risk getting left behind. Organizations understand that data is a key strategic asset which, when combined with the scale and intelligence of cloud, can provide the opportunity to automate, innovate, and increase the speed of business. But every migration journey is unique, so knowing the tricks of the trade will make your journey far easier. In this session, we use real-world case studies to provide details about how to perform large-scale migrations. We also share information about how Microsoft is investing in making this journey simpler with Azure Database Migration Service and related tools.: – Azure SQL Database is Microsoft’s fully managed, database-as-a-service offering based on the world’s top relational database management system, SQL Server. In this session, learn about the latest innovations in Azure SQL Database and how customers are using our managed service to modernize their applications.

Our most recent version combines advanced intelligence, enterprise-grade performance, high-availability, and industry-leading security in one easy-to-use database. Thanks to innovations such as In-Memory OLTP, Columnstore indexes, and our most recent Adaptive Query Processing feature family, customers can rely on Azure SQL DB for their relational data management needs, from managing just a few megabytes of transactional data.: See how to use the latest SSIS 2017 to modernize traditional on-premises ETL workflows, transforming them into scalable hybrid ETL/ELT workflows. We showcase the latest additions to SSIS Azure Feature Pack, introducing/improving Azure connectivity components, and take a deep dive into SSIS Scale-Out feature, guiding you end-to-end from cluster installation to parallel execution, to help reduce the overall runtime of your workflows.

Finally, we show you how to orchestrate/schedule SSIS executions using Azure Data Factory (ADF) and share our cloud-first product roadmap towards SSIS Platform as a Service (PaaS).: This session focuses on the needs of the data integrator whether that be for data warehousing/BI, advanced analytics or preparation of data for SaaS applications. We walk through, by example, a comprehensive set of new additions to Azure Data Factory to make moving and integrating data across on-premises and cloud simple, scalable and reliable. Topics covered include: how to lift SSIS packages to the cloud via first-class SSIS support in data factory, a new serverless data factory application model and runtime capabilities, parallel data movement to/from the cloud, a new code-free experience for building and monitoring data pipelines and more.: Data is the new Electricity, and Big Data technologies are helping organizations leverage this new phenomena to foster their businesses in innovative ways. In this session, we show how you can leverage the big data services such as Data Warehousing, Hadoop, Spark, Machine Learning, and Real Time Analytics on Azure and how you can make the most of these for your business scenarios. This is a foundational session to ground your understanding on the technology, its use cases, patterns, and customer scenarios. You will see a lot of these technologies in action and get a good view of the breadth. Join this session if you want to get a real understanding of Big Data on Azure, and how the services are structured to achieve your desired outcome.: Come learn to understand real world challenges associated with building a complex, large-scale data warehouse in the cloud.

Learn how technologies such as Low Latency Analytical Processing [LLAP] and Hive 2.x are making it better by dramatically improved performance and simplified architecture that suites the public clouds. In this session, we go deep into LLAP’s performance and architecture benefits and how it compares with Spark and Presto. We also look at how business analysts can use familiar tools such as Microsoft Excel and Power BI, and do interactive query over their data lake without moving data outside the data lake.: You are already familiar with the key value propositions of Apache Spark. In this session, we cover new capabilities coming in the latest versions of Spark. More importantly we cover how customers are using Apache Spark for building end-to-end data analytics pipeline. It starts from ingestion, Spark streaming, and then goes into the details on data manipulation and finally getting your data ready for serving to your BI analysts.: Azure Blob Storage is the exa-scale object storage service for Microsoft Azure. In this session, we cover new services and features including the brand new Archival Storage tier, dramatically larger storage accounts, throughput and latency improvements and more.

We give you an overview of the new features, present use cases and customer success stories with Blob Storage, and help you get started with these exciting new improvements.: Increasingly, customers looking to modernize their analytics needs are exploring the data lake approach. They are challenged by poorly-integrated technologies, a variety of data formats, and inconvenient data types. We explore a modern ETL pipeline through the lens of Azure Data Lake.

This approach allows pipelines to scale to thousands of nodes instantly and lets pipelines integrate code written in.NET, Python, and R. This degree of extensibility allows pipelines to handle formats such as CSV, XML, JSON, Images, etc. Finally, we explore how the next generation of ETL scenarios are enabled by integrating intelligence in the data layer in the form of built-in cognitive capabilities.: Earlier this year, we announced Azure Cosmos DB – the first and only globally distributed, multi-model database system.

The service is designed to allow customers to elastically and horizontally scale both throughput and storage across any number of geographical regions, it offers guaranteed. There are a tremendous amount of Microsoft products that are cloud-based for building big data solutions. It’s great that there are so many products to choose from, but it does lead to confusion on what are the best products to use for particular use cases and how do all the products fit together. My job as a Microsoft Cloud Solution Architect is to help companies know about all the products and to help them in choosing the best products to use in building their solution.

Based on a recent architect design session with a customer I wanted to list the products and use cases that we discussed for their desire to build a big data solution in the cloud (focusing on compute and data storage products and not ingestion/ETL, real-time streaming, advanced analytics, or reporting; also, only solutions are included – no ): • (ADLS): Is a high throughput distributed file system built for cloud scale storage. It is capable of ingesting any data type from videos and images to PDFs and CSVs. This is the “landing zone” for all data. It is HDFS compliant, meaning all products that work against HDFS will also work against ADLS. Think of ADLS as the place all other products will use as the source of their data. All data will be sent here including on-prem data, cloud-based data, and data from IoT devices.

This landing zone is typically called the Data Lake and there are many great reasons for using a Data Lake (see and and the presentation ) • (HDI): Under the covers, HDInsight is simply that contains 22 open source products such as Hadoop (Common, YARN, MapReduce), Spark, HBase, Storm, and Kafka. You can use any of those or install any other open source products that can all use the data in ADLS (HDInsight just connects to ADLS and uses that as its storage source) • (ADLA): This is a distributed analytics service built on Apache YARN that lets you submit a job to the service where the service will automatically run it in parallel in the cloud and scale to process data of any size. Included with ADLA is, which has a scalable distributed query capability enabling you to efficiently analyze data whether it be structured (CSV) or not (images) in the Azure Data Lake Store and across Azure Blob Storage, SQL Servers in Azure, Azure SQL Database and Azure SQL Data Warehouse. Note that U-SQL supports batch queries and does not support interactive queries, and does not handle or indexing.

ADLA is great for things such as prepping large amounts of data for insertion into a data warehouse or replacing long-running monthly batch processing with shorter running distributed processes • (AAS): This is a PaaS for SQL Server Analysis Services (SSAS). It allows you to create an Azure Analysis Services Tabular Model (i.e. Cube) which allows for much faster query and reporting processing compared to going directly against a database or data warehouse. A key AAS feature is vertical scale-out for and high concurrency. It also creates a semantic model over the raw data to make it much easier for business users to explore the data. It pulls data from the ADLS and aggregates it and stores it in AAS. The additional work required to add a cube to your solution involves the time to process the cube and slower performance for ad-hoc queries (not pre-determined), but there are additional benefits of a cube – see • (SQL DW): This is a SQL-based, fully-managed, petabyte-scale cloud data warehouse.

It’s highly elastic, and it enables you to set up in minutes and scale capacity in seconds. You can scale compute and storage independently, which allows you to burst compute for complex analytical workloads. It is an technology that shines when used for ad-hoc queries in relational format. It requires data to be copied from ADLS into SQL DW but this can be done quickly using.

Compute and storage are separated so you can pause SQL DW to save costs (see ) •: This is a globally distributed, multi-model (key-value, graph, and document) database service. It fits into the NoSQL camp by having a non-relational model (supporting schema-on-read and JSON documents) and working really well for large-scale OLTP solutions (it also can be used for a data warehouse when used in combination with Apache Spark – a later blog). See and the presentation. It requires data to be imported into it from ADLS using Azure Data Factory •: This is a search-as-a-service cloud solution that gives developers APIs and tools for adding a rich full-text search experience over your data. You can store indexes in Azure Search with pointers to objects sitting in ADLS. Azure Search is rarely used in data warehouse solutions but if queries are needed such as getting the number of records that contain “win”, then it may be appropriate. Azure Search supports a that crawls a supported data source such as Azure Blob Storage or and automatically uploads the data into your index.

It also supports the for other data sources such as ADLS to programmatically send the data to Azure Search to make it available for searching. Note that Azure Search is built on top of and uses the •: This is an enterprise-wide metadata catalog that makes data asset discovery straightforward. It’s a fully-managed service that lets you register, enrich, discover, understand, and consume such as ADLS. It is a single, central place for all of an organization’s users to contribute their knowledge and build a community and culture of data. Without using this product you will be in danger having a lot of data duplication and wasted effort •: This is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project.

It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with Azure Blog Storage, ADLS, SQL DW, Cosmos DB, Azure Event Hub, and Power BI. Think of it as an alternative to HDI and ADLA. It differs from HDI in that HDI is a PaaS-like experience that allows working with more OSS tools at a less expensive cost. Databricks advantage is it is a Spark-as-a-Service-like experience that is easier to use, has native Azure AD integration (HDI security is via Apache Ranger and is Kerberos based), has auto-scaling and auto-termination, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark. Also note with built-in integration to SQL DW it can write directly to SQL DW, as opposed to HDInsight which cannot and therefore more steps are required: when HDInsight processes data it writes it back to Blob Storage and then requires ADF to move the data from Blob Storage to SQL DW In addition to ADLS, can be used instead of ADLS or in combination with it. When comparing ADLS with Blob storage, Blob storage has the advantage of lower cost since there are now three Azure Blob storage tiers:,, and, that are all less expensive than ADLS.

The advantage of ADLS is that there are no limits on account size and file size (Blob storage has a 5 PB account limit and a 4.75 TB file limit). ADLS is also faster as files are auto-sharded/chunked where in Blob storage they remain intact. ADLS supports Active Directory while Blob storage supports. ADLS also supports while Blob storage does not (it supports WASB which is a thin layer over Blob storage that exposes it as a HDFS file system). Finally, while Blob storage is in all, ADLS is only in two US regions (East, Central) and North Europe (other regions coming soon). Now that you have a high-level understanding of all the products, the next step is to determine the best combination to use to build a solution.

If you want to use Hadoop and don’t need a relational data warehouse the product choices may look like this: Most companies will use a combination of HDI and ADLA. The main advantage with ADLA over HDI is there is nothing you have to manage (i.e. Performance tuning), there is only one language to learn (U-SQL) with easier development and debugging tools, you only incur costs when running the jobs where HDI clusters are always running and incurring costs regardless if you are processing data or not, and you can scale individual queries independently of each other instead of having queries fight for resources in the same HDIinsight cluster (so predictable vs unpredictable performance). In addition, ADLA is always available so there is no startup time to create the cluster like with HDI. HDI has an advantage in that it has more products available with it (i.e.

Kafka) and you can customize it (i.e. Install additional software) where in ADLS you cannot. When submitting a U-SQL job under ADLA you specify the resources to use via a.

Currently, an AU is the equivalent of 2 CPU cores and 6 GB of RAM and you can go as high as 450 AU’s. For HDI you can give more resources to your query by increasing the number of in a cluster (limited by the region max core count per subscription but you can contact billing support to increase your limit). Most of the time a relational data warehouse as part of your solution, with the biggest reasons being familiarity with relational databases by the existing staff and the need to present an easier to understand presentation layer to the end-user so they can create their own reports (self-service BI). A solution that adds a relational database may look like this: The Data Lake technology can be ADLS or blob storage, or even Cosmos DB. The main reason against using Cosmos DB as your Data Lake is cost and having to convert all files to JSON. A good reason for using Cosmos DB as a Data Lake is that it enables you to have a single underlying datastore that serves both operational queries (low latency, high concurrency, low compute queries – direct from Cosmos DB) as well as analytical queries (high latency, low concurrency, high compute queries – via Spark on Cosmos DB). By consolidating to a single data store you do not need to worry about data consistency issues between maintaining multiple copies across multiple data stores.

Additionally, Cosmos DB has disaster recovery built-in by easily allowing you to replicate data across Azure regions with automatic failover (see ), while ADLS requires replication and failover to be done manually (see ). Blob storage has disaster recovery built-in via (GRS) but requires manual failover by Microsoft (see ). An option to save costs is to put “hot” data in Cosmos DB, and warm/cold data in ADLS or Blob storage while using the same reporting tool, Power BI, to access the data from either of those sources as well as many others (see and ). If Cosmos DB is your data lake or used as your data warehouse (instead of SQL DW/DB in the picture above), you can perform ad-hoc queries using familiar SQL-like grammar over JSON documents (including like SUM) without requiring explicit schemas or creation of secondary indexes. This is done via the REST API, JavaScript,.NET, Node.js, or Python.

Querying can also be done via on Azure HDInsight, which provides additional benefits such as faster performance and SQL statements such as GROUP BY (see ). Check out the to run sample queries on Cosmos DB using sample data. Note the query results are in JSON instead of rows and columns.

You will need to determine if your solution will have dashboard and/or ad-hoc queries. Your choice of products in your solution will depend on the need to support one or both of those queries. For ad-hoc queries, you have to determine what the acceptable performance is for those queries as that will determine if you need a or solution (see ).

For dashboard queries (i.e. From PowerBI) it’s usually best to have those queries go against AAS to get top-notch performance and because SQL DW has a (and one dashboard can have a dozen or so queries). Another option to get around the 32-concurrent query limit is to copy data from SQL DW to data marts in. Complex queries, sometimes referred to as “last mile” queries, may be too slow for a SMP solution (i.e.

SQL Server, Azure SQL Database) and require a MPP solution (i.e. The diagram above shows SQL DW or Azure SQL Database (SQL DB) as the data warehouse. To decide which is the best option, see. With a clustered column store index SQL DB competes very well in the big data space, and with the addition of R/Python stored procedures, it becomes one of the fastest performing machine learning solutions available. But be aware that the max database size for SQL DB is 4 TB, but there will soon be an option called SQL DB Managed Instance that supports a max database size much higher. See the presentations and.

You will also need to determine if you solution will have batch and/or interactive queries. All the products support batch queries, but ADLA does not support interactive queries (so you could not use the combination of Power BI and ADLA).

If you want to stay within the Hadoop world you can use the HDInsight cluster types of or for interactive queries against ADLS or Blob Storage (see ) and can use instead of AAS to build cubes/OLAP within Hadoop. AtScale will work against data in ADLS and Blob Storage via HDInsight. Whether to have users report off of ADLS or via a relational database and/or a cube is a balance between giving users data quickly and having them do the work to join, clean and master data (getting IT out-of-the-way) versus having IT make multiple copies of the data and cleaning, joining and mastering it to make it easier for users to report off of the data. The risk in the first case is having users repeating the process to clean/join/master data and cleaning/joining/mastering it wrong and getting different answers to the same question (falling into the old mistake that the data lake does not need data governance and will magically make all the data come out properly – not understanding that HDFS is just a glorified file folder). Another risk in the first case is performance because the data is not laid out efficiently. Most solutions incorporate both to allow “power users” to access the data quickly via ADLS while allowing all the other users to access the data in a relational database or cube. Digging deeper, if you want to run reports straight off of data in ADLS, be aware it is file-based security and so you may want to create a cube for row-level security and also for faster performance since ADLS is a file system and does not have indexes (although you can use a products such to create indexes within ADLS/HDFS).

Also, running reports off of ADLS compared to a database has disadvantages such as limited support of concurrent users; lack of indexing, metadata layer, query optimizer, and memory management; no support or data integrity; and security limitations. If using Power BI against ADLS, note that Power BI imports the data from ADLS. It is not a live connection to ADLS so you could easily run into the (although potentially not until after ingesting 10GB of data from ADLS as Power BI can achieve compression of up to 10 times). And be aware the initial import can be slow and the data would be “stale” until refreshed again. The decisions on which products to use is a balance between having multiple copies of the data and the additional costs that incurs and the maintaining and learning of multiple products versus less flexibility in reporting of data and slower performance. Also, while incorporating more products into a solution means it takes longer to build, the huge benefit of that is you “future proof” your solution to be able to handle any data in the future no matter what the size, type, or frequency.

The bottom line is there are so many products with so many combinations of putting them together that a blog like this can only help so much – you may wind up needing a solution architect like me to help you make sense of it all 🙂 More info: My presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation Presentation. Many product announcements were made this week at, and I wanted to give a quick overview of all the data platform related announcements: • SQL Server 2017 on Linux, Windows, and Docker, generally available on October 2nd. SQL Server 2017 is being released simultaneously for Windows and various flavors of Linux: Red Hat Enterprise Linux 7.3, SUSE Linux Enterprise Server 12, Ubuntu and Docker. The official Docker image is based on Ubuntu 16.04. The performance of SQL Server on Linux vs Windows is “”.

However, not everything has been ported. There are no Reporting Services or Analysis Services, nor Machine Learning Services, transactional replication, Stretch DB, or File Table (see ). Management tools remain for the most part Windows only, though command-line tools work. The major new features are,,, and for better query optimization. Learn and see • Azure Database Migration Service (DMS) and Azure SQL Database Managed Instance, public preview. New Managed Instance offering within SQL Database offers near-complete SQL Server compatibility and network isolation for easiest lift and shift to Azure.

DMS offers a fully managed, first party Azure service that enables customers to easily migrate their on-premises SQL server databases to Azure SQL Database Managed Instance and SQL Server in Azure Virtual Machines with minimal to no downtime. Customers can maximize existing license investments with discounted rates on Managed Instance using a new Azure Hybrid Benefit for SQL Server. For news on availability • Azure Machine Learning, new capabilities in public preview. Updates connect every element of the data science process with enhanced productivity and collaboration for AI developers and data scientists at any scale. Enables them to start building right away with their choice of tools and frameworks.

The updated platform includes a enhanced data cleansing and prepping tool called ML Workbench to start the modeling process sooner. It is a client application that runs on Windows and Mac and is targeted at data scientists who are not users of Visual Studio and integrates with popular open source data science toolkits such as Python Scikit Learn, Jupyter Notebooks and Matplotlib. It integrates with the cloud by seamlessly moving the heavy lifting to the GPU-powered VMs in Azure. Other new capabilities include The Azure Machine Learning Experimentation service allows developers and data scientists to increase their rate of experimentation; The Model Management service provides deployment, hosting, versioning, management, and monitoring for models in Azure, on-premises, and to IOT Edge devices. These new features will help data scientists develop, deploy, and manage machine learning and AI models at any scale wherever data lives: in the cloud, on-premises, and edge.

Learn more on the page and • updates. Includes general availability of Text Analytics API, a cloud-based service for language processing such as sentiment analysis, key phrase extraction and language detection. In October, we will also make generally available Bing Custom Search to create customized search experience for a section of the web, and Bing Search APIs v7 for searching the entire web for more relevant results using Bing Web, News, Video & Image search. Read the announcement • Announcing the preview of with R support in Azure SQL Database.

You can evaluate this preview functionality in any server/database created in the West Central US Region. • Azure Data Factory (ADF) – announcing new capabilities in public preview. These new capabilities in ADF will enable you to build hybrid data integration at scale. Now you can create, schedule, and orchestrate your ETL/ELT workflows, wherever your data lives, in the cloud or on any self-hosted network. Meet security and compliance needs while taking advantage of extensive capabilities and paying only for what you use. Accelerate your data integration with multiple data source connectors natively available in-service.

SQL Server Integration Services (SSIS) customers will benefit from easily lifting their SSIS packages into the cloud using new managed SSIS hosting capabilities in Data Factory. We have taken the first steps to separate Control Flow and Data Flow within ADF to provide greater control over complex orchestrations that now facilitate looping, branching, and conditional structures within Control Flow. We have added new flexibility to scheduling by enabling triggering with wall-clock timers or on-demand via event generation. Parameters can now be defined and passed while invoking pipelines to enable incremental data loads. If you want to move your SSIS workloads, you can create a data factory version 2, and provision an Azure-SSIS Integration Runtime (IR). The Azure-SSIS IR is a fully managed cluster of Azure VMs (nodes) dedicated to run your SSIS packages in the cloud. For step-by-step instructions, see the tutorial:.

Full details of the release and features can be found on the page. We encourage you to, available at public preview pricing • Announcing the preview for the Azure Data Box. A hardware appliance that companies can use to load their data for shipping to the closest Microsoft Azure data center. The 45-lb box, which is tamper proof, holds up to 100 terabytes (TB) of data.

It plugs into a corporate network for downloads, and then into Azure’s own high-speed networks to upload its contents. Companies will be able to rent it, fill it, and ship it while tracking its progress. Data on the device will be encrypted throughout the journey. • Introducing Azure Availability Zones for resiliency and high availability.

Availability Zones are fault-isolated locations within an Azure region, providing redundant power, cooling, and networking. Availability Zones allow customers to run mission-critical applications with higher availability and fault tolerance to datacenter failures.

• Public preview: Virtual network service endpoints for Azure Storage and SQL Database. You can now secure Azure Storage and Azure SQL Database to only your virtual networks, by using virtual network service endpoints. Endpoints provide a direct connection from your virtual network to the Azure services, extending your virtual network’s private address space and identity to the services. Traffic from your virtual network to the services will always remain on the Microsoft Azure network backbone. • Intelligent insights for Azure SQL Database. Azure SQL Database built-in intelligence continuously monitors database usage through artificial intelligence and detects disruptive events that cause poor performance. Once detected, a detailed analysis is performed generating a diagnostic log with intelligent assessment of the issue.

This assessment consists of a root cause analysis of the database performance issue and where possible recommendations for performance improvements. • Read replicas for Azure Database for MySQL. Read replicas will allow customers using MySQL on-premises or on other cloud service providers to create replicas of their instance in Azure. They can then choose to upgrade the replica to master in Azure Database for MySQL, and connect their apps directly to the new database instance. If you are interested in understanding the functionality of this private preview visit the or more information • Renamed R Server to Machine Learning Server. Announced was the renaming of Microsoft R Server to Microsoft Machine Learning Server and SQL Server R Services to SQL Server Machine Learning Services. The additional language support aligns the Advanced Analytics workload to Machine Learning capabilities and focus on AI.

With Python support in addition to R and Microsoft ML libraries we enhance Machine Learning capabilities and offer the ability to develop new intelligent applications combining the best of open source and enterprise capabilities of SQL Server 2017. • Azure SQL Database: Vulnerability Assessment. SQL Vulnerability Assessment (currently in preview) is an easy to configure tool that can discover, track, and remediate potential database vulnerabilities.

Use it to proactively improve your database security. • The Power BI team announced a much-awaited feature; automatic updates to the Power BI Desktop. Through the Windows Store, you can now install the Power BI Desktop once and get updates automatically every month. Read on to learn more • Faster compute optimized performance tier for Azure SQL Data Warehouse. The compute optimized performance tier brings several benefits to your analytics workloads.

The first benefit can be seen through dramatically improved query performance. Individual query execution times have improved by as much as 10x. We’ve also seen some fantastic results with customer workloads and benchmarks where queries are completing twice as fast on average.

The compute and storage scalability has also been dramatically increased with this performance tier. You can now provision 5x the computing power and store an unlimited amount of columnar data, empowering you to run your largest and most complex analytics workloads.

• Azure free account, now available. A best-in-industry offer, the Azure free account helps customers try Azure. It comes with 12-months free access to compute, storage, database, and networking services, along with 25+ always-free services, including Azure App Service and Functions.

It also includes a $200 credit allowing customers to try any Azure product for the first 30 days. More information at and • Azure Stack, now shipping through Dell EMC, HPE, and Lenovo. Azure Stack is an extension of Azure, allowing customers to uniquely meet hybrid requirements like compliance, latency, and true consistency as a part of their hybrid cloud strategy. Cisco and Wortmann will start taking orders soon. Customers can also buy Azure Stack as a managed service from Avanade, Rackspace, and several MSP partners. Azure Stack certification for IT Professionals materials are available now, and certifications exams will start Q1 2018.

Amore A Prima Svista Italy Earthquake. More information on • Azure Reserved Virtual Machine Instances. When available later in 2017, customers will be able to reserve virtual machines on Azure for a one- or three-year term with significant cost savings of up to 82% over pay-as-you-go prices when combined with Azure Hybrid Benefit and up to 72% on all VMs.

Customers select the VM type, term, and datacenter region, so the compute resources are available when and where needed. Improve budgeting with a single up-front payment while maintaining the flexibility to exchange or cancel at any time. Details on • Native integration between Azure Cosmos DB and Azure Functions. We’re bringing the power of Azure Cosmos DB to our serverless offering, Azure Functions. With this integration, developers can write serverless apps backed by Cosmos DB, with just a few lines of code. They can innovate faster by reacting in real-time to changes happening in the database to drive more engaging and personalized customer experiences.

Using Azure Functions and Azure Cosmos DB, customers can create and deploy event-driven, planet-scale serverless apps with extremely low-latency access against very rich data. Read the • GA of HDInsight Interactive Query (Hive LLAP). This is an Azure HDInsight. It supports in-memory caching, which makes Hive queries faster and much more interactive. • Microsoft is now offering Blob storage accounts with up to 5PB (petabytes) of maximum capacity, a 10x increase.

Both incoming and outgoing data can now move at up to 50Gbps (gigabits per second) and users can expect 50,000 TPS/IOPS (transactions per second/input output operations per second) performance, a 2.5x jump. • Announcing new Azure VM sizes for more cost-effective database workloads. We are excited to announce the latest versions of our most popular VM sizes (DS, ES, GS, and MS), which constrain the vCPU count to one half or one quarter of the original VM size, while maintaining the same memory, storage and I/O bandwidth. We have marked these new VM sizes with a suffix that specifies the number of active vCPUs to make them easier for you to identify. For example, the current VM size Standard_GS5 comes with 32 vCPUs, 448GB mem, 64 disks (up to 256 TB), and 80,000 IOPs or 2 GB/s of I/O bandwidth.

The new VM sizes Standard_GS5-16 and Standard_GS5-8 comes with 16 and 8 active vCPUs respectively, while maintaining the rest of the specs of the Standard_GS5 in regards to memory, storage, and I/O bandwidth. • New in Stream Analytics: Output to Azure Functions, built-in anomaly detection, etc.

Announced the preview of several new and compelling capabilities in Azure Stream Analytics. These include built-in inline machine learning based anomaly detection, egress to Azure functions, support for compressed data formats, JavaScript User defined aggregates, and support for CI/CD in Visual Studio tooling. These new features will start rolling out over the course of the next several weeks. • Announcing Azure Migrate. A new service that provides the guidance, insights, and mechanisms needed to assist you in migrating on-premises virtual machines and servers to Azure. In SQL Server, scaling out reads (i.e.

Using ) is a lot easier than scaling out writes. So what are your options when you have a tremendous amount of writes that scaling up will not handle, no matter how big your server is? There are a number of options that allow you to write to many servers (instead of writing to one master server) that I’ll call distributed writes. Here are some ideas: • Peer-to-Peer transactional replication (or Multi-master replication) with SQL Server. See • Sharding in via elastic database tools which requires coding. You can also for SQL Server • Merge replication in SQL Server.

See • Create a messaging and queuing application in where all writes are placed on the queue and sent to different servers • Create a message queue using an asynchronous • Use a 3rd party product: for SQL Server, • Instead of using SQL Server, use a NoSQL or multi-model database service like (no coding involved – think of it as auto-sharding) The one option out of all the above options that does not require coding and can support a large number of writes per second is Azure Cosmos DB. All the other options can require significant coding and/or can only handle a limited amount of writes per second. This is because Cosmos DB uses documents (JSON files) where all the information needed is included in that document so no joins are needed and documents can be spread on multiple servers (see and ). This is opposed to relational databases that use multiple tables that must be joined. If the tables are on different nodes that will cause a lot of data shuffling causing performance problems.

To go into greater detail on the benefits of Cosmos DB over SQL Server for distributed writes: • Consistency • Peer2Peer SQL Replication introduces pains around data consistency and conflict resolution • Availability • Sharding with SQL introduces pains around maintaining availability when increasing/decreasing the degree of scale-out. Frequently, downtime is involved due to needs to re-balancing data across shards • SQL requires rigid schemas and indices to be defined upfront.

Every time schema and index updates are needed – you will incur a heavy operational cost of running Create Index and Alter Tables scripts across all database shards and replicas. Furthermore, this introduces availability issues as schemas are being altered. • Handling sustained heavy write ingestion • Putting queueing mechanisms in front of SQL only gives you a buffer for handling spikes in writes, but at the end of the day, the database itself needs to support sustained heavy write ingestion in order to consume the buffered events. What happens if events come in to the buffer faster than you drain it? You will need a database specifically designed for heavy write ingestion Azure Cosmos DB solves these by: • Providing 5 well-defined consistency models to help developers tune the right Consistency vs Performance tradeoffs for their scenario • Scale on demand and support for flexible data model while maintaining high availability (99.99% availability SLA). Scaling out and partition management is taken care of by the service on behalf of the user • Use of log-structured techniques to be a truly latch-free database to sustain heavy write ingestion with durable persistence In the end, eliminating schema, index management, and JOINs are a necessary byproduct of scale out that Azure Cosmos DB provides. After the initial post of this blog, I received the question “Why not just use SQL 2016 in-Memory tables for heavy write systems ()?” and received a great reply from a Cosmos DB product manager: SQL in-memory is only viable when: • Request and data volume are small enough to fit on a single machine.

You still have the fundamental problem of hard limits due to scale-up model. • Scenario does not need durability, reliability, or availability – which are requirements for >99% of mission critical customer scenarios.

Durability • If data is kept in only in-memory, you experience data loss upon any intermittent failure that requires computer to restart (e.g. Os crash, power outage, os decides it wants to reboot to update, etc.). In order for data to be durable, it needs to be persisted to disk. In order to offer resiliency against disk failures, you will need to replicate to a number of disks • For durable scenarios – memory only acts as a buffer to absorb spikes.

In order to achieve sustained write ingestion – you will need to flush the buffer as fast as you input into the buffer. Now you have a bottleneck on disk i/o unless you scale-out • This is why they immediately have to address that this is for “applications where durability is not required”; durability is a requirement for >99% of data scenarios • Data loss and data corruption should be treated as cardinal sin for any data product Scale • This model is still a scale-up model – in which there are many hard limits • What happens for data volume that doesn’t fit in memory (which tends to be very small size relative to disk storage)? You need to scale-out • What happens for request volume that memory bandwidth is inadequate? You need to scale out • This is why the throughput numbers in the blog are orders of magnitude smaller than what customers are doing everyday on Cosmos DB, and talking about storage size is quietly ignored Expensive • Memory is 100x more expensive than SSD. Achieving high storage in a scale-out system will yield not only better scale and durability characteristics – but incur much lower costs for any large-scale scenarios More info.