Azure Data Architecture Guide (ADAG)

The Azure Data Architecture Guide has just been released!  Check it out:

Think of it as a menu or syllabus for data professionals.  What service should you use, why, and when would you use it.  I had a small involvement in its creation, but there were a large number of people within Microsoft and from 3rd parties that put it together over many months.  Hopefully you find this clears up some of the confusion caused by so many technologies and products.

“This guide presents a structured approach for designing data-centric solutions on Microsoft Azure.  It is based on proven practices derived from customer engagements.”

You can even download a PDF version (106 pages!).

The guide is structured around a basic pivot: The distinction between relational data and non-relational data:

Within each of these two main categories, the Data Architecture Guide contains the following sections:

  • Concepts. Overview articles that introduce the main concepts you need to understand when working with this type of data.
  • Scenarios. A representative set of data scenarios, including a discussion of the relevant Azure services and the appropriate architecture for the scenario.
  • Technology choices. Detailed comparisons of various data technologies available on Azure, including open source options.  Within each category, we describe the key selection criteria and a capability matrix, to help you choose the right technology for your scenario.

The table of contents looks like this:

Traditional RDBMS



Big data and NoSQL



Cross-cutting concerns

Posted in Big Data, Data warehouse, SQLServerPedia Syndication | Leave a comment

Conversations with Data Warehouse Experts – Podcast

In this podcast I talk with Mike Rabinovici of Dimodelo Solutions about data being the new currency, the importance of showing customers the art of the possible, and last but not least my go to TV show.  Click here to listen.  Also check out the podcasts of other data warehouse experts.

Posted in Podcast, SQLServerPedia Syndication | 2 Comments

Data Virtualization vs. Data Movement

I have blogged about Data Virtualization vs Data Warehouse and wanted to blog on a similar topic: Data Virtualization vs. Data Movement.

Data virtualization integrates data from disparate sources, locations and formats, without replicating or moving the data, to create a single “virtual” data layer that delivers unified data services to support multiple applications and users.

Data movement is the process of extracting data from source systems and bringing it into the data warehouse and is commonly called ETL, which stands for extraction, transformation, and loading.

If you are building a data warehouse, should you move all the source data into the data warehouse, or should you create a virtualization layer on top of the source data and keep it where it is?

The most common scenario where you would want to do data movement is if you will aggregate/transform one time and query the results many times.  Another common scenario is if you will be joining data sets from multiple sources frequently and the performance needs to be super fast.  These turn out to be the scenarios for most data warehouse solutions.  But there could be cases where you will have many ad-hoc queries that don’t need to be super fast.  And you could certainty have a data warehouse that uses data movement for some tables and data virtualization for others.

Here is a comparison of both:

Other data virtualization benefits:

  • Provides complete data lineage from the source to the presentation layer
  • Additional data sources can be added without having to change transformation packages or staging tables
  • All data presented through the data virtualization software is available through a common SQL interface regardless of the source (i.e. flat files, Excel, mainframe, SQL Server, etc)

While this table gives some good benefits of data virtualization over data movement, it may not be enough to overcome the sacrifice in performance or other drawbacks listed at Data Virtualization vs Data Warehouse.  Also keep in mind the virtualization tool you choose may not support some of your data sources.

The better data virtualization tools provide such features as query optimization, query pushdown, and caching (i.e. Denodo) that may help with performance.  You may see tools with these features called “data virtualization” and tools without these features called “data federation” (i.e. PolyBase).

More info:


Posted in SQLServerPedia Syndication, Virtualization | 1 Comment

Reference architecture for enterprise reporting in Azure

As I mentioned in my recent blog Use cases of various products for a big data cloud solution, with so many products it can be difficult to know the best products to use when building a solution.  When it comes to building an enterprise reporting solution, there is a recently released reference architecture to help you in choosing the correct products.  It will also help you get started quickly as it includes an implementation component in Azure.  The blog post announcement is here.

This reference architecture is focused solely on reporting, for those use cases where you will have a lot of users building dashboards via Power BI and operational reports via SSRS.  You can certainly expand the capabilities to add more features such as machine learning as well as enhancing the purpose of certain products, such as using Azure SQL Data Warehouse (SQL DW) to accept large ad-hoc queries from users.  The reference architecture is also for a batch-type environment (i.e. loading data every hour) and not a real-time environment (i.e. handling thousands of events per second).

Key features and benefits include:

  • Pre-built based on selected and stable Azure components proven to work in enterprise BI and reporting scenarios
  • Easily configured and deployed to an Azure subscription within a few hours
  • Bundled with software to handle all the operational essentials for a full-fledged production system
  • Tested end-to-end against large workloads
  • You can operationalize the infrastructure using the steps in the User’s Guide, and explore component level details from the Technical Guides.  Also, check out the FAQ

You can one-click deploy the infrastructure implementation from one of these two locations, which also go into details on each step in the above diagram:

The idea is you are deploying a base architecture, then you will modify as needed to fit all your needs.  But the hard work of choosing the right products and building the starting architecture is done for you, reducing your risk and shortening development time.  However, this does not mean you should use these chosen products in every situation.  For example, if you are comfortable with Hadoop technologies you can use Azure Data Lake Store and HDInsight instead of SQL DW, or use Azure Analysis Services (AAS) instead of SQL Server Analysis Services (SSAS) in a VM (AAS did not support VNETs when this reference architecture was created).  But for many who just need an enterprise reporting solution, this will do the job with little modification.

Note the Cortana Intelligence Gallery has many others solutions so be sure to check them out and avoid “reinventing the wheel”.

Posted in Power BI, SQLServerPedia Syndication, SSAS, SSRS | 2 Comments

Is the traditional data warehouse dead?

There have been a number of enhancements to Hadoop recently when it comes to fast interactive querying with such products as Hive LLAP and Spark SQL which are being used over slower interactive querying options such as Tez/Yarn and batch processing options such as MapReduce (see Azure HDInsight Performance Benchmarking: Interactive Query, Spark and Presto).

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 Why You Need a Data Warehouse).  And what is meant by a “traditional” data warehouse is usually referring to a relational data warehouse built using SQL Server (if using Microsoft products) and when a data lake is mentioned it is usually one that is built in Hadoop using Azure Data Lake Store (ADLS) and HDInsight (which has cluster types 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 Hadoop and Data Warehouses so this is a good time to provide an update.  I also touched on this topic in my blogs Use cases of various products for a big data cloud solutionData lake detailsWhy use a data lake? and What is a data lake? and my presentation Big data architectures and the data lake.  

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 WinterCorp report 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 Microsoft Products vs Hadoop/OSS Products).  Building a data warehouse solution on the cloud or migrating to the cloud is often the best idea (see To Cloud or Not to Cloud – Should You Migrate Your Data Warehouse?) 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.  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 self-service BI 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 Why use a SSAS cube?).

Relational data warehouses continue to meet the information needs of users and continue to provide value.  Many people use them, depend on them, trust 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. not having full support for ANSI SQL such as certain aggregate functions which limits the range of users, tools, and applications that can access Hadoop data) and it still isn’t quite at the performance level that a relational database can provide
  • Hadoop lacks a sophisticated query optimizer, in-database operators, advanced memory management, concurrency, dynamic workload management and robust indexing strategies and therefore performs poorly for complex queries
  • Hadoop does not have the ability to place “hot” and “cold” data on a variety of storage devices with different levels of performance to reduce cost
  • Hadoop is not relational, as all the data is in files in HDFS, so there is always a conversion process to convert the data to a relational format if a reporting tool requires it in a relational format
  • Hadoop is not a database management system.  It does not have functionality such as update/delete of data, referential integrity, statistics, ACID compliance, data security, and the plethora of tools and facilities needed to govern corporate data assets
  • There is no metadata stored in HDFS, so another tool such as a Hive Metastore needs to be used to store that, adding complexity and slowing performance.  And most metastores only work with a limited number of tools, requiring multiple metastores
  • Finding expertise in Hadoop is very difficult: The small number of people who understand Hadoop and all its various versions and products versus the large number of people who know SQL
  • Hadoop is super complex, with lot’s of integration with multiple technologies to make it work
  • Hadoop has many tools/technologies/versions/vendors (fragmentation), no standards, and it is difficult to make it a corporate standard.  See all the various Apache Hadoop technologies here
  • Some reporting tools don’t work against Hadoop
  • May require end-users to learn new reporting tools and Hadoop technologies to query the data
  • The newer Hadoop solutions (Tez, Spark, Hive LLAP etc) are still figuring themselves out.  Customers might not want to take the risk of investing in one of these solutions that may become obsolete (like MapReduce)
  • It might not save you much in costs: you still have to purchase hardware or pay for cloud consumption, support, licenses, training, and migration costs.  As relational databases scale up, support non-standard data types like JSON, and run functions written in Python, Perl, and Scala, it makes it even more difficult to replace them with a data lake as the migration costs alone would be substantial
  • If you need to combine relational data with Hadoop, you will need to move that relational data to Hadoop or invest in a technology such as PolyBase to query Hadoop data using SQL
  • Is your current IT experience and comfort level mostly around non-Hadoop technologies, like SQL Server?  Many companies have dozens or hundreds of employees that know SQL Server and not Hadoop so therefore would require a ton of training as Hadoop can be overwhelming

As far as performance, it is greatly affected by the use of indexing – Hive with LLAP (or not) doesn’t have indexing, so when you run a query, it reads all of the data (minus partition elimination).  Spark SQL, on the other hand, isn’t really an interactive environment – it’s fast-batch – so again, not going to see the performance users will expect from a relational database.  Also, a relational database still beats most competitors when performing complex, multi-way joins.  Given that most analytic queries are just that, a traditional data warehouse still might be the right choice.

From a security standpoint, you would need to integrate Hive LLAP or Spark with Apache Ranger to support granular security definition at the column level, including data masking where appropriate.

Concurrency is another thing to think about – Hadoop clusters have to get VERY large to support hundreds or thousands of concurrent connections – remember, these systems aren’t designed for interactive usage – they are optimized for batch and we are trying to shoehorn interactivity on top of that.

A traditional relational data warehouse should be viewed as just one more data source available to a user on some very large federated data fabric.  It is just pre-compiled to run certain queries very fast.  And a data lake is another data source for the right type of people.  A data lake should not be blocked from all users so you don’t have to tell everyone “please wait three weeks while I mistranslate your query request into a new measure and three new dimensions in the data warehouse”.

Most data lake vendors assume data scientists or skilled data analysts are the principal users of the data.  So, they can feed these skilled data users the raw data.  But most business users get lost in that morass.  So, someone has to model the data so it makes sense to business users.  In the past, IT did this, but now data scientists and data analysts can do it using powerful, self-service tools.  But the real question is: does a data scientist or analyst think locally or globally?  Do they create a model that supports just their use case or do think more broadly how this data set can support other use cases?  So it may be best to continue to let IT model and refine the data inside a relational data warehouse so that it is suitable for different types of business users.

I’m not saying your data warehouse can’t consist of just a Hadoop data lake, as it has been done at Google, the NY Times, eBay, Twitter, and Yahoo.  But are you as big as them?  Do you have their resources?  Do you generate data like them?  Do you want a solution that only 1% of the workforce has the skillset for?  Is your IT department radical or is it conservative?

I think a relational data warehouse still has an important place: performance, ease of access, security, integration with reporting components, and concurrency all lean towards using it, especially when performing complex, multi-way joins that make up analytic queries which is the sweet spot for a traditional data warehouse.

The bottom line is a majority of end users need the data in a relational data warehouse to easily do self-service reporting off of it.  A Hadoop data lake should not be a replacement for a data warehouse, but rather should augment/complement a data warehouse.

More info:

Is Hadoop going to Replace Data Warehouse?


The Demise of the Data Warehouse

Counterpoint: The Data Warehouse is Still Alive

The Future of the Data Warehouse

Whither the Data Warehouse? Reflections From Strata NYC 2017

Big Data Solutions Decision Tree

Dimensional Modeling and Kimball Data Marts in the Age of Big Data and Hadoop

Hadoop vs Data Warehouse: Apples & Oranges?


Posted in Data Lake, Data warehouse, SQLServerPedia Syndication | 10 Comments

What is Azure Databricks?

Azure Databricks (documentation and user guide) 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 Spark Data Sources).  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 auto-scaling and auto-termination (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: Sign up for the Azure Databricks limited preview

More info

Microsoft makes Databricks a first-party service on Azure


Microsoft Launches Preview of Azure Databricks

A technical overview of Azure Databricks

Microsoft Azure Debuts a ‘Spark-as-a-Service’


Posted in Azure Data Lake Analytics, Azure Databricks, HDInsight, SQLServerPedia Syndication | 1 Comment

Microsoft Connect(); announcements

Microsoft Connect(); is a developer event from Nov 15-17, where plenty of announcements are made.  Here is a summary of the data platform related announcements:

  • 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.  More info
  • 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.  More info.  See all Cosmos DB announcements
  • 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.  More info
  • 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.  More info
  • 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.  More info
Posted in Azure Cosmos DB, Azure SQL Database, SQLServerPedia Syndication | 1 Comment

Analytics Platform System (APS) AU6 released

Better late than never: The Analytics Platform System (APS), which is a renaming of the Parallel Data Warehouse (PDW), released an appliance update (AU6) about a year ago, and I missed the announcement.  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 level 130 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.


APS 2016 supports a broader set of T-SQL compatibility, including support for wider rows and a large number of rows, VARCHAR(MAX)NVARCHAR(MAX) and VARBINARY(MAX).  For greater analysis flexibility, APS supports full window frame syntax for ROWS or RANGE and additional windowing functions like FIRST_VALUELAST_VALUECUME_DIST and  PERCENT_RANK.  Additional functions like NEWID() and RAND() work with new data type support for UNIQUEIDENTIFIER and NUMERIC.  For the full set of supported T-SQL, please visit the online documentation.

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 SQL Server with Fast Track for Data Warehouse or Azure SQL Data Warehouse, a cloud based fully managed service.

Next Steps

For more details about these features, please visit our online documentation or download the client tools.

Posted in PDW/APS, SQLServerPedia Syndication | Comments Off on Analytics Platform System (APS) AU6 released

Azure SQL Database Managed Instance

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 here.  If you are not familiar with Azure SQL Database, first check out my introduction presentation.

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 presentation at Ignite by Drazen Sumic called “Modernize your on-premises applications with SQL Database Managed Instances (BRK2217)” and this blog post by Lindsey Allen.

There was also a presentation 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.

More info:

Native database backup in Azure SQL Managed Instance

Top Questions from New Users of Azure SQL Database

Managed Instances versus Azure SQL Database—What’s the Right Solution for You?

Posted in Azure SQL Database, SQLServerPedia Syndication | 4 Comments

Microsoft Ignite Big Data Presentations

There were so many good presentations at Microsoft Ignite, 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 (Use cases of various products for a big data cloud solution) can be used as a summary of all these options:

Modernize your on-premises applications with SQL Database Managed Instances: 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.

Architect your big data solutions with SQL Data Warehouse and Azure Analysis Services: 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.

Database migration roadmap with Microsoft: 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.

What’s new with Azure SQL Database: Focus on your business, not on the database: – 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.

Deep dive into SQL Server Integration Services (SSIS) 2017 and beyond: 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).

New capabilities for data integration in the cloud: 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.

Understanding big data on Azure – structured, unstructured and streaming: 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.

Building Petabyte scale Interactive Data warehouse in Azure HDInsight: 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.

Building modern data pipelines with Spark on Azure HDInsight: 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: Scalable, efficient storage for PBs of unstructured data: 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.

Modernizing ETL with Azure Data Lake: Hyperscale, multi-format, multi-platform, and intelligent: 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.

Azure Cosmos DB: The globally distributed, multi-model database: 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 <10 ms latencies at the 99th percentile, 99.99% high availability and five well defined consistency models to developers. It’s been powering Microsoft’s internet-scale services for years. In this session, we present an overview of Azure Cosmos DB—from global distribution to scaling out throughput and storage—enabling you to build highly scalable mission critical applications.

First look at What’s New in Azure Machine Learning: Take in the huge set of capabilities announced at Ignite for the next generation of the Azure Machine Learning platform. Build and deploy ML applications in the cloud, on-premises, and at the edge. Get started by wrangling your data into shape easily and efficiently, then take advantage of popular tools like Cognitive Toolkit, Jupyter, and Tensorflow to build advanced ML models and train them locally or at large scale in the cloud. Learn how to deploy models with a powerful, new, Docker-based hosting service complete with the ability to monitor and manage everything in production.

Delivering enterprise BI with Azure Analysis Services: Learn how to deliver analytics at the speed of thought with Azure Analysis Services on top of a petabyte-scale SQL Data Warehouse and Azure HDInsight Spark implementation. This session covers best practices for managing processing and query accelerating at scale, implementing change management for data governance, and designing for performance and security. These advanced techniques are demonstrated through an actual implementation including architecture, code, data flows, and tips and tricks.

Creating enterprise grade BI models with Azure Analysis Services: Microsoft Analysis Services enables you to build comprehensive, enterprise-scale analytic solutions that deliver actionable insights through familiar data visualization tools such as Microsoft Power BI and Microsoft Excel. Analysis Services enables consistent data across reports and users of Power BI. This session covers new features such as improved Power BI Desktop feature integration, Power Query connectivity, and techniques for modeling and data loading which enable the best reporting experiences. Various modeling enhancements are included, such as Detail Rows allowing users to easily see transactional records; and deployment and application-lifecycle management (ALM) features to bridge the gap between self-service and corporate BI.

Streaming big data on azure with HDInsight, Kafka, Storm, and Spark: Implementing big data streaming pipelines for robust, enterprise use cases is hard. Doing so with open source technologies is even harder. To help with this, HDInsight recently added Kafka as a managed service to complete a scalable, big data streaming scenario on Azure. This service processes millions+ of events/sec, pedabytes of data/day to power scenarios like Toyota’s connected car, Office 365’s clickstream analytics, fraud detection for large banks, etc. We will discuss the streaming landscape, challenges in building production ready streaming services, and build an enterprise grade realtime pipeline. We will then discuss the learnings and future investments on managed open source streaming through Azure HDInsight.

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