SQL Bits 2019 Keynote

What an amazing SQLBits in Manchester. Four days packed full of leading edge data technology covering

• SQL Server 2019 Big Data
• Azure SQL Managed Database
• Power BI
• Kubernetes
• Machine Learning
• Python
• Spark

This year SQLBits 2019 had a keynote.  It was nice for the event to have a keynote again. The theme Data Never Rests.  The Microsoft Data Platform Product group who spoke were Buck Woody, Bob Ward, Anna Thomas, Alain Dormehl, Adam Saxton and Patrick LeBlanc. An amazing set of speaks and fun keynote. They shared details of the evolution of the data platform to enable people to keep their skills up to date. The keynote is available to watch . There were several major announcements.

SQL Server 2019 will RTM in second half of the year. SQL Server 2019 CTP2.3 is available now with

• Big data cluster enhancements
• Accelerated database recovery
• Performance enhancements
• Graph data enhancements
• SSAS enhancements

SQL Server 2019 is a modern innovation and there are various forms of the product.

• On Premises
• SQL Server Azure VM (IaaS)
• Azure SQL DB Managed Instance (PaaS)
• Azure SQL Data Warehouse

Azure SQL Database Hyperscale can autoscale up to 100TB and scale compute and storage independently.

During the keynote they showed Azure SQL Database Hyperscale where a 50TB database was restored in just under 8 minutes. That is nice accelerated database recovery.

Data virtualization and big data clusters is a game changing view with SQL Server 2019 big data clusters, data lake scale, machine learning and AI. Multiple data sources can be connected using external table, through the compute pool using Polybase connectors at the source.  Data persistence using multiple data sources is stored in shards of the data pool for SQL Server 2019 big data clusters data mart.

SQL 2019 will send push down predicated queries to other data platforms via Polybase to join SQL data with Oracle, Mongodb and CosmosDB data in one place efficiently.

SQL notebooks in Azure Data Studio is an awesome new feature. 

There is documentation to read and new courses for learning.

aka.ms/DataAccessGuide

and a Summary of All Exams and Certifications Launched in January, 2019!

aka.ms/DataEngCerts

PASS Summit 2018 Keynote Day 1

The first keynote of PASS summit was delivered by Rohan Kumar entitled SQL Server and Azure Data Services: Harness the ultimate hybrid platform for data and AI. 

Customer priorities for a modernized data estate are: modernizing on-premises, modernizing to cloud, build cloud native apps and unlocking insights.

The announcements follow:

SQL Server 2019

SQL Server 2019 Public Preview  is a great way to celebrate the 25th anniversary of SQL Server. 

There is the introduction of big data clusters which combines Apache Spark and Hadoop into a single data platform called SQL Server. This combines the power of Spark with SQL Server over the relational and non-relation data sitting in SQL Server, HDFS and other systems like Oracle, Teradata, CosmosDB.

There are new capabilities around performance, availability and security for mission critical environments along with capability to leverage hardware innovations like persistent memory and enclaves.

Hadoop, ApacheSpark, Kubernetes and Java are native capabilities in the database engine.

Accelerated data recovery (ADR) was demonstrated and is incredible. It is at public preview.  The benefits of ADR are

• Fast and consistent Database Recovery
• Instantaneous Transaction rollback
• Aggressive Log Truncation

Azure HDInsight 4.0

HDInsight 4.0 is now available in public preview.

There are several Apache Hadoop 3.0 innovations. Hive LLAP (Low Latency Analytical Processing known as Interactive Query in HDInsight) delivers ultra-fast SQL queries. The Performance metrics provide useful insight.

Integration with Power BI direct Query, Apache Zeppelin, and other tools. To learn more HDInsight Interactive Query with Power BI.

Data quality and GDPR compliance enabled by Apache Hive transactions

Improved ACID capabilities handle data quality (update/delete) issues at row level. This means that GDPR compliance requirements can now be meet with the ability to erase the data at row level. Spark can read and write to Hive ACID tables via Hive Warehouse Connector.

Apache Hive LLAP + Druid = single tool for multiple SQL use cases

Druid is a high-performance, column-oriented, distributed data store, which is well suited for user-facing analytic applications and real-time architectures. Druid is optimized for sub-second queries to slice-and-dice, drill down, search, filter, and aggregate event streams. Druid is commonly used to power interactive applications where sub-second performance with thousands of concurrent users are expected.

Hive Spark Integration

Apache Spark gets updatable tables and ACID transactions with Hive Warehouse Connector

There are several Apache Hadoop 3.0 innovations. Hive LLAP (Low Latency Analytical Processing called Interactive Query in HDInsight) for ultra-fast SQL queries. The Performance metrics provide useful insight.

Integration with Power BI Direct Query, Apache Zeppelin, and other tools. To learn more watch HDInsight Interactive Query with Power BI.

Better data quality and GDPR compliance enabled by Apache Hive transactions

Improved ACID capabilities handle data quality (update/delete) issues at row level. GDPR compliance requirements can now be meet with the ability to erase the data at row level. Spark can read and write to Hive ACID tables via Hive Warehouse Connector

Apache Hive LLAP + Druid = single tool for multiple SQL use cases

Druid is a high-performance, column-oriented, distributed data store, which is suited for user-facing analytic applications and real-time architectures. Druid is optimized for sub-second queries to slice-and-dice, drill down, search, filter, and aggregate event streams. Druid is commonly used to power interactive applications where sub-second performance with thousands of concurrent users are expected.

Hive Spark Integration

Apache Spark gets updatable tables and ACID transactions with Hive Warehouse Connector.

Apache HBase and Apache Phoenix

Apache HBase 2.0 and Apache Phoenix 5.0 get new performance and stability features and all of the above have enterprise grade security.

Azure

Azure event hubs for Kafka is generally available

Azure Data Explorer is in public preview.

Azure Databricks Delta is in public preview

• Connect data scientist and engineers
• Prepare and clean data at massive scales
• Build/train models with pre-configured ML

Azure Cosmos DB multi master replication was demoed with a drawing app, Azure Cosmos DB PxDraw

Azure SQL DB Managed Instances will be at General Availability (GA) on Dec 1^st. This provides Availability Groups managed by Microsoft.

Preview of Machine Learning Services with R support in Azure SQL Database

Power BI

Public Preview of Paginated Reports in Power BI Premium Now Available

Power BI delivers dataflows, enterprise reporting, and major updates to Power BI Desktop

The new Dataflows is an enabler for self-service data prep in Power BI

Power BI Desktop November Update

• Follow-up questions for Q&A explorer. It is possible to ask follow-up questions inside the Q&A explorer pop-up, which take into account the previous questions you asked.
• Copy and paste between PBIX files
• New modelling view makes it easier to work with large models.
• Expand and collapse matrix row headers

Apache Calcite: A Foundational Framework

At ACM SIGMOD/PODS 2018, this week, Hortonworks are talking about Calcite.  A foundational framework for optimized query processing over heterogeneous data sources. It seems an interesting dynamic data management framework that omits some key functions: storage of data, algorithms to process data, and a repository for storing metadata.  

The main goal was to originally improve Apache Hive in three different axes: latency, scalability, and SQL support. Hive and Calcite are more integrated now and the new features for its optimizer aimed to generate better plans for query execution.

It can be used for data virtualization/federation. It supports heterogeneous data models and stores (relational, semi-structured, streaming, and geospatial). This flexible, embeddable, and extensible architecture is an attractive choice for adoption in big-data frameworks.

Comparison of big data engines

A comparison of big data querying engines is below.

Apache HBase is the Hadoop database, a distributed, scalable, big data store. HBase is an open-source, non-relational, distributed database modelled after Google's Bigtable and is written in Java.

Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop.

Splunk turns machine data into answers with the leading platform to tackle the toughest IT, IoT and security challenges.

More details of the comparison HBase vs. Hive vs. Splunk

Apache Hive and HDInsight

Apache Hive is a data warehouse system for Hadoop. Hive enables data summarization, querying, and analysis of data. Hive queries are written in HiveQL, which is a query language similar to SQL.  Hive only maintains metadata information about your data stored on HDFS. Apache Spark has built-in functionality for working with Hive and HiveQL can be used to query data stored in HBase. Hive can handle large data sets. The data must have some structure. The query execution can be via Apache Tez, Apache Spark, or MapReduce.

There are two types of tables within Hive.

• Internal: Data is stored in the Hive data warehouse. The data warehouse is located at /hive/warehouse/ on the default storage for the cluster. This is for mainly temporary data.
• External: data is stored outside the data warehouse. The data is also used outside of Hive or the data needs to stay in the underlying location

A Hive table consists of a schema stored in the metastore and the data is stored on HDFS. The supported file formats are Text File, SequenceFile, RCFile, Avro Files, ORC Files, Parquet, Custom INPUTFORMAT and OUTPUTFORMAT.

The Hive architecture is

Apache Hive and Hive QL is on Azure HDInsight.