The Data Lakehouse is your next cloud Data Warehouse

Modern businesses generate a colossal amount of data—customer information, supply chain details, purchase analytics, and more. Tapping into this wealth of information can give your business an edge and increase profits.

However, much of this data goes untapped mainly because it's in different formats and spread across various platforms. Often, making the most of such data comes with enormous financial and technical implications, way out of reach for many businesses.

If you wish to harness the power of big data without breaking the bank, Databricks SQL can help.

Here is the graphical representation of Databricks SQL’s  high-performance (formerly SQL Analytics)

Note: SQL Warehouse cost includes both HCP360 and IDNA Reports, whereas Redshift cost includes only IDNA Reports.

What is Databricks SQL      

Databricks SQL is an innovative, cloud-based analytics and data processing solution to help you leverage the power of SQL and Apache Spark™ in data analysis. It's built on top of Apache Spark, an open-source data processing engine, to deliver a powerful, unified platform that supports flawless collaboration.

Databricks SQL allows users to ingest, store, and process data from various sources, including databases, data lakes, and streaming data sources. The powerful SQL engine supports complex SQL queries, joins, and aggregates data sets to improve an organization's ability to handle big data.

Since the innovative platform supports machine learning and advanced data analytics, users can leverage in-built libraries and algorithms for predictive modeling, clustering, and natural language processing.  

Databricks SQL Tool for Reference

Working in Databricks SQL is an easy transition for your team members.  Views like these below show how the user experience is similar to other data warehouses. 

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The differentiation for Databricks SQL 

Databricks SQL provides businesses with a robust, scalable, cost-effective cloud data warehousing and analytics solution. It aims to help companies overcome traditional data warehousing and analytics challenges.

With Databricks SQL, businesses have a unified analytics platform that fosters collaboration. You can streamline your data analytics workflow and have your data teams collaborate on the same platform. It eliminates the need for multiple, expensive tools and platforms, which can prove a logistical nightmare.

Databricks SQL allows you to harness the power of big data and data lakes for business intelligence. You can effortlessly scale your data warehousing, and analytics infrastructure as your data volumes grow since Databricks SQL analytics is built on top of Apache Spark.  

Databricks SQL lowers the entry barrier to help businesses harness the power of cloud-based data warehousing without incurring high upfront costs. The solutions offer a pay-as-you-go pricing models to help you lower and optimize your costs while maximizing your ROI.

Top Use Cases

As a powerful and innovative business data solution, Databricks SQL with a Data Lakehouse has many applications. Some of the top uses include:

• Business intelligence and analytics: It supports sophisticated data exploration, visualization, and reporting to help you gain valuable insights from your data.
• Data warehousing: It doubles as a cloud-based data warehousing solution, powering your ability to store, process, and analyze large volumes of structured and unstructured data.
• Advanced business analytics: In-built libraries and algorithms let you harness the power of predictive modeling, machine learning, and natural language processing without specialized expertise. Databricks SQL Analytics reduces complex data analytics to a few clicks of a button.
• Metadata management: It integrates with Databricks Unity Catalog to help you gain valuable insights from your metadata. The Unity Catalog provides a unified view of the metadata spread across various tools and data sources. It lets you discover data assets, understand data lineage, and organize and manage metadata. Besides enabling team collaboration, The Unity Catalog provides data automation capabilities to reduce manual effort.

Unique Features for Choosing Databricks SQL      

Some of the unique features that make Databricks SQL analytics a standout choice for cloud data warehousing and analytics include:

• Unified analytics platform: It enables collaboration and seamless integration across different workflows and users functions.
• Scalability: It's built on top of Apache Spark, providing immense data processing capabilities when handling big data.
• Open standards and interoperability: Supports open standards and readily integrates with other data tools and platforms.
• Enterprise-grade security: Comes with advanced security features, including encryption, data governance, and access control to ensure data privacy and compliance with regulatory requirements.
• Cost-effective: Databricks provides a highly optimized platform that reduces the cost of ETL by eliminating the need to copy data into a different data warehouse. The platform's optimization and data locality deliver greater performance at lower cost, while the pay-as-you-go pricing model enables customers to easily get started and demonstrate the platform's business value before making a commitment.

• Centralized storage and governance with standard SQL: Users can have a single copy of all their data using open format Delta Lake. You will easily secure, discover and manage data with precise governance and standard SQL across clouds.     

• Unity Catalog : Databricks Unity Catalog is an integrated governance solution for all AI and data assets. This includes dashboards, machine learning models, tables, and files in your Lakehouse on any cloud.

 It works with existing governance solutions, data storage systems, and catalogs,     allowing users to leverage their investments and build a robust governance model without high migration costs.

Unity Catalog makes data governance and secure data sharing easier. It allows users to configure and integrate access control permissions.

Scaling Your Workloads with Databricks Serverless Compute

Databricks Serverless Compute is a powerful and flexible platform that allows you to scale your workloads on demand without specialized infrastructure. It provides you with scalable and cost-effective data processing capabilities. Databricks Serverless Compute can help you scale your workloads by      

• Autoscaling: It automatically prorates computing resources based on the work demand, so you only pay for what you use.
• Automatic workload isolation: Isolates workloads to ensure operational efficiency since each workload gets the necessary resources.
• Flexible resource allocation: You can allocate resources, including CPU, memory, and I/O, to meet specific workload requirements.
• Parallel Processing: By leveraging Apache Sparks, Databricks Serverless enables parallel data processing across multiple nodes.  

Databricks SQL and Third-party BI Tools     

Databricks SQL supports third-party BI tools allowing you to leverage existing tools and workflows. It readily integrates with popular tools such as Power BI, Tableau, Looker, and QlikView.

You may use standard SQL and JDBC/ODBC drivers to Databricks SQL with your preferred BI tools to query and analyze data. Databricks SQL Analytics integrates seamlessly into your existing data analytics workflow without needing to learn new processes or purchase new tools.

Databricks SQL also supports tool-specific connectors and integrations. Native tool connectors allow for flawless integrations when performing analytics. For instance, you use Tableau's native connector to connect the two and visualize real-time data.  

How Does It Work?

Databricks SQL enables you to analyze and visualize data using standard SQL queries. It's built on the Apache Spark™ platform to provide a distributed processing engine ideal for large-scale data processing and analytics.

Here's how Databricks SQL works:

• You can use data from various sources, including data lakes, databases, and streaming data sources. SQL supports various file formats, including parquet, CSV, and JSON.
• Data preparation: Databricks SQL offers various data preparation tools for cleansing, transformation, and enrichment. You may use SQL queries or visual tools to prepare data for analysis.
• SQL queries: Use the in-built SQL engine to run SQL queries on the ingested and prepared data. You can run the queries in a notebook interface or through REST API.
• Data visualization: Databricks SQL features in-built data visualization tools such as graphs, charts, and dashboards. You can create custom visualization using BI tools such as Tableau, Power BI, and Looker.
• Collaboration: Databricks SQL provides a collaborative environment. Your teams can collaborate on various projects by sharing notebooks, queries, and visualization. It also supports real-time collaboration through in-built chat and commenting features.

Case Study

How a F500 Pharma company reduced 30% of data warehousing cost by implementing Databricks SQL          

Client Challenge: 

• Maintaining the same data set in multiple places like Delta Lake as well as on the data warehouse creates additional costs for clients     . 
• Redshift takes a longer time to run SQL Queries compared to Databricks SQL, which impacts the Tableau Dashboard refresh time.
• The company experienced difficulty in accessing data from different systems and accessing ETL instantly.

Agilisium’s Solution:

• All reports are created using Tableau Dashboards. Earlier, these dashboards were connected to the AWS Redshift warehouse to pull the processed data after ETL.
• ETL jobs used to load data into Redshift to make it available for Dashboards.
• All ETL jobs in the application run in Databricks. DBSQL can connect with both Delta Lake and Tableau.
• With Databricks SQL analytics we don’t have to keep the same data in two places.
• We have migrated 300+ dashboards from Redshift to SQL Analytics.

Business Benefits:

• Tableau dashboard refresh time improved by 30% and cost was reduced by 30% with the implementation of Databricks SQL analytics. 
• Additional cost of Databricks Cluster used for Redshift Data Load is waived off.
• Processed data is instantly accessible for analytics after ETL
• Query runs using Spark, hence Spark features helps to improve performances. 

e.g Dynamic Partition Pruning, In-Memory computation, etc.

• Utilizes Databricks Photon Cluster which helps to run queries faster.
• Databricks SQL enables high bandwidth data transfers to BI tools with Cloud Fetch. Cloud Fetch allows extracts and large result queries to transfer much faster, reducing the uptime of compute.

Conclusion

Databricks SQL aims to transform data warehousing and analytics to help businesses make better-informed decisions. It provides a unified platform for data management, analysis, and collaboration. An affordable subscription model eliminates the exorbitant upfront costs to harness the power of cloud-based computing and make data-driven decisions. If you are considering Databricks SQL, contact us today and start your digital transformation journey with us.