Sql Server 2019 New Features

SQL Server 2019 Features: An Overview

Today I will describe the new features and performance enhancements to security in SQL Server 2019 introduced by Microsoft at the Ignite Conference 2018 in Orlando, Florida. Today’s SQL Server 2019 not only works in Windows or Linux but also multiple OS types and it is integrated with multiple data sources which will be discussed below. Now it can be utilized in Machine Learning and Artificial Intelligence.

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SQL Server 2019 Features and Performance

SQL Server 2019 will help us by making data integration, Management, and intelligence easier and more intuitive than ever earlier.

Major Key points

• Single virtual data layer
• Data virtualization and Integrating Data
• No data replication and Managing all data
• Spark Built-In
• Unified platform for big data analytics
• Spark jobs
• Train machine learning models

In SQL Server 2019 you can create a single virtual data layer and all applications can access it. Polybase data virtualization reduces the complexity of data source integration without moving it.

The Data management process can be easier using SQL Server 2019 big data clusters which can be deployed in Kubernetes. Every node of the big data cluster includes SQL server relational engine HDFS storage and spark which will allow you to store and manage the data.

We can build intelligent apps with big data now. You can run spark jobs to analyze the structured and unstructured data. Train models over data from everywhere with SQL server machine learning services or spark ml and query data anywhere using a highly configurable notebook embedded in Azure Data Studio. The SQL Server 2019 makes it easier to manage and handle business data and keep it secure than ever before.

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1. SQL Server 2019 Features with Azure Data Studio and Spark

Here I will tell you how we can use Sql Server 2019 and spark together as a unified platform running on Kubernetes and how Azure Data Studio provides a seamless experience over data. SQL server 2019 deployed in Kubernetes provides more flexibility to run on-premises or in the cloud. In the same instance of Azure Data Studio, we can connect two sql server instances to the spark HDFS endpoint in the Kubernetes cluster.

SQL Server 2019 provides a unified view of enterprise data whether it is relational data stored in data sources or big data stored in HDFS clusters. SQL Server 2019 allows querying data from other data sources such as Oracle, Teradata, and MongoDB.

2. Data Virtualization from Oracle

Data virtualization provides data quality, data security, and data privacy. Once I choose the data to virtualize I can write a new simple SQL query which will query the results from my remote server in Oracle. Once the data is in the SQL server I can write a simple select top thousand records that query the data which is in Oracle.

3. Spark and HDFS Endpoint running on Kubernetes

In SQL server 2019 the SQL engine is there to read files located in HDFS. If one file is uploaded in HDFS, write SQL query to query directly from the file which is stored in HDFS. Once we create external tables over the files in HDFS. We can easily join the data with other relational data sources. In this way, SQL Server 2019 joins high-value data in relational databases with a high volume of data in HDFS.

Sql server also provides scalable computing and storage for faster data processing. SQL Server 2019 is the first release where we are bringing both SQL and spark together providing query capabilities over scalable storage across relational and big data.

4. Notebook Viewer In Azure Data Studio

In Azure Data Studio, I can easily browse my files in HDFS. With one click I can start analyzing my files in a notebook. In Azure Data Studio, we have an integrated notebook viewer that seamlessly connects SQL Server 2019 clusters. It is attached to the PI spark kernel and lets you submit the spark jobs against the cluster.

5. Azure Data Studio made it easy for Data Scientists

Data scientists spend a lot of time to prepare the data. Using Azure Data Studio we have made it easier for data scientists to be more productive.

6. Spark job viewer

In Azure Data Studio it has made it simple for customers to submit spark jobs against the cluster. It has built a rich spark job viewer which will allow customers to monitor their submitted spark jobs.

7. Data Migration and Management

There are more than 340 types of databases and moving and replication data across was a big challenge. Integrating data across them is always very difficult but SQL Server 2019 integrates data from many data sources without movement. That means using one platform we can access multiple data sources.

8. SQL Server 2019 meets Big Data Explosion

Read and write directly in HDFS using Sql server and Spark. Using Kubernetes architecture we can scale computing and storage as per demand. It is possible to combine and cache data from relational and non-relational data sources with scale-out data marts.

9. SQL Server 2019 brings an easy platform for analytic users

SQL Server 2019 enables analytic users to build intelligent apps and leverage AI from their data. Query your data using your programming languages like Java, python, scala, etc in an SQL server or Spark. To train data models we can use Sql server machine Learning and Spark ML. The big advantage is we can store and operationalize all train models.

SQL Server 2019 Big Data Clusters

SQL Server Big Data Clusters offer deep integration between SQL Server and big data in a form easy to deploy and manage. It offers four major pieces of functionality:

1. Data virtualization

Combine data from many sources without moving or replicating it.

2. Managed SQL Server, Spark, and data lake

It can save high-volume data and access it easily using either SQL or Spark. It can handle and manage easily in the field of Management services, admin portal, and integrated security.

3. Support 100% AI platform

It can easily integrate data from all different Data sources into your model. Next, it can train, store, and your models all under one platform.

4. Easy job for data scientists to manage Big Data

Data scientists earlier fetched high-value data from the enterprise database to retrieve data from big data and push it into Hadoop, As a result, they were able to join it with the new data streams.

SQL Server Big Data Clusters provide a platform for big data sets that can be joined with the dimensional and fact data that are stored in the enterprise database and provide a comfortable way for users and applications that can use SQL Server to query big data.

Azure Data Studio and Other Tools In SQL Server 2019

Azure Data Studio is an open-source, cross-platform desktop application that can be used for querying SQL Server instances running on different platforms, Azure SQL Data Warehouse, and Azure SQL Database instances. Azure Data Studio and Visual Studio Code are both from the same platform, and they contain Git integration. It makes it easy for Database Administrators and data scientists to interact with SQL Server Big Data Clusters using Azure Data Studio and Jupyter Notebooks.

To know more about it visit http://aka.ms/azuredatastudio Azure Data Studio and SQL Operations Studio are both based on the same platform and Azure Data Studio was released based on SOS.

Why Should We Prefer SQL Server 2019?

The Most important part of today’s data management world is the product should work in multiple languages, cross-platform systems, and multiple data sources under one box. Sql Server 2019 covers all these above features and fulfills the demands of end-users under one platform and that’s why we should use Sql Server 2019. SQL Server 2019 has a big role in the field of managing big data and provides an easy and unique platform for data scientists to handle data using Azure Data Studio.

Let’s discuss this in detail

1. Automatic plan correction resolves performance problems called Automatically Tune Sql server.
2. Sql server provides layers of security with protection and it is always in encrypted secure enclaves.
3. Database maintenance can be reduced by running on available groups on containers using Kubernetes.
4. Data discovery and classification for GDPR and vulnerability assessment tool to track compliance.
5. It can support Windows, Linux, and containers using Kubernetes. You can run Java code in an SQL server to store and analyze graph data.
6. Sql server has control and makes use of the power of big data.
7. AI platform now trains models in Sql server ML services using Azure data studio notebooks.
8. Data virtualization allows queries across different data sources by eliminating data movement and replication.
9. Visual data integration and analysis using SQL server BI tool and Power BI report server.
10. It can run real-time analytics on operational data using HTAP.
11. Azure Data Studio has a big role in cross-platform for querying SQL server instances.

SUMMARY

• Introduction

• Features and performance

• SQL Server 2019 Big Data Clusters

• Azure Data Studio and tools introduced in SQL Server 2019

• Why should we prefer SQL Server 2019?

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