Manage and analyze humongous amounts of data with SQL Server 2019 Big Data Cluster
Mon, 22 Apr 2024 11:50:19 -0000
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A collection of facts and statistics for reference or analysis is called data, and, in a way, the term “big data” is a large sum of data. The big data concept has been around for many years, and the volume of data is growing like never, which is why data is a hugely valued asset in this connected world. Effective big data management enables an organization to locate valuable information with ease, regardless of how large or unstructured the data is. The data is collected from various sources including system logs, social media sites, and call detail records.
The four V's associated with big data are Volume, Variety, Velocity, and Veracity:
- Volume is about the size—how much data you have.
- Variety means that the data is very different—that you have very different types of data structures.
- Velocity is about the speed of how fast the data is getting to you.
- Veracity, the final V, is a difficult one. The issue with big data is that it is very unreliable.
SQL Server Big Data Clusters make it easy to manage this complex assortment of data.
You can use SQL Server 2019 to create a secure, hybrid, machine learning architecture starting with preparing data, training a machine learning model, operationalizing your model, and using it for scoring. SQL Server Big Data Clusters make it easy to unite high-value relational data with high-volume big data.
Big Data Clusters bring together multiple instances of SQL Server with Spark and HDFS, making it much easier to unite relational and big data and use them in reports, predictive models, applications, and AI.
In addition, using PolyBase, you can connect to many different external data sources such as MongoDB, Oracle, Teradata, SAP HANA, and more. Hence, SQL Server 2019 Big Data Cluster is a scalable, performant, and maintainable SQL platform, data warehouse, data lake, and data science platform that doesn’t require compromising between cloud and on-premises. Components include:
Controller | The controller provides management and security for the cluster. It contains the control service, the configuration store, and other cluster-level services such as Kibana, Grafana, and Elastic Search. |
Compute pool | The compute pool provides computational resources to the cluster. It contains nodes running SQL Server on Linux pods. The pods in the compute pool are divided into SQL compute instances for specific processing tasks. |
Data pool | The data pool is used for data persistence and caching. The data pool consists of one or more pods running SQL Server on Linux. It is used to ingest data from SQL queries or Spark jobs. SQL Server Big Data Cluster data marts are persisted in the data pool. |
Storage pool | The storage pool consists of storage pool pods comprising SQL Server on Linux, Spark, and HDFS. All the storage nodes in a SQL Server Big Data Cluster are members of an HDFS cluster. |
Following is the reference architecture of SQL Server 2019 on Big Data Cluster:
Big data analysis
Data analytics is the science of examining raw data to uncover underlying information. The primary goal is to ensure that the resulting information is of high data quality and accessible for business intelligence as well as big data analytics applications. Big Data Clusters make machine learning easier and more accurate by handling the four Vs of big data:
The impact of the Vs on analytics | How a Big Data Cluster helps | |
Volume | The greater the volume of data processed by a machine learning algorithm, the more accurate the predictions will be. | Increases the data volume available for AI by capturing data in scalable, inexpensive big data storage in HDFS and by integrating data from multiple sources using PolyBase connectors. |
Variety | The greater the variety of different sources of data, the more accurate the predictions will be. | Increases the number of varieties of data available for AI by integrating multiple data sources through the PolyBase connectors. |
Velocity | Real-time predictions depend on up to-date data flowing quickly through the data processing pipelines. | Increases the velocity of data to enable AI by using elastic compute and caching to speed up queries. |
Veracity | Accurate machine learning depends on the quality of the data going into the model training. | Increases the veracity of data available for AI by sharing data without copying or moving data, which introduces data latency and data quality issues. SQL Server and Spark can both read and write into the same data files in HDFS. |
Cluster management
Azure Data Studio is the tool that data engineers, data scientists, and DBAs use to manage databases and write queries. Cluster admins use the admin portal, which runs as a pod inside the same namespace as a whole cluster and provides information such as status of all pods and overall storage capacity.
Azure Data Studio is a cross-platform management tool for Microsoft databases. It’s like SQL Server Management Studio on top of the popular VS Code editor engine, a rich T-SQL editor with IntelliSense and plug-in support. Currently, it’s the easiest way to connect to the different SQL Server 2019 endpoints (SQL, HDFS, and Spark). To do so, you need to install Data Studio and the SQL Server 2019 extension.
If you have a Kubernetes infrastructure, you can deploy this with a single server cluster in single command and have a cluster in about 30 minutes.
If you want to install SQL Server 2019 Big Data Cluster on your on-premises Kubernetes cluster, you can find an official deployment guide for Big Data Clusters on Minikube in Microsoft docs.
Conclusion
Planning is everything and good planning will get a lot of problems out of the way, especially if you are thinking about streaming data and real-time analytics.
When it comes to technology, organizations have many different types of big data management solutions to choose from. Dell Technologies solutions for SQL Server help organizations achieve some of the key benefits of SQL Server 2019 Big Data Clusters:
- Insights to everyone: Access to management services, an admin portal, and integrated security in Azure Data Studio, which makes it easy to manage and create a unified development and administration experience for big data and SQL Server users
- Enriched data: Data using advanced analytics and artificial intelligence that’s built into the platform
- Overall data intelligence:
- Unified access to all data with unparalleled performance
- Easily and securely manage data (big/small)
- Build intelligent apps and AI with all data
- Management of any data, any size, anywhere: Simplified management and analysis through unified deployment, governance, and tooling
- Easy deployment and management of using Kubernetes-based big data solution built into SQL Server
To make better decisions and to gain insights from data, large, small, and medium-size enterprises use big data analysis. For information about how the SQL solutions team at Dell help customers store, analyze, and protect data with Microsoft SQL Server 2019 on Big Data Cluster technologies, see the following links:
https://www.delltechnologies.com/en-us/big-data/solutions.htm#dropdown0=0
https://infohub.delltechnologies.com/t/sql-server/