Real-Time Insights: The Top Three Reasons Why Customers Love Data Streaming with Databricks

The world operates in real-time

The ability to make real-time decisions in today's fast paced world is more critical than ever before. Today's organizations need to react instantly to events and quickly access and analyze data in order to get real-time insights and make informed decisions. Simplifying the process of making real-time decisions across different use cases ultimately accelerates innovation, reduces costs, and greatly improves customer experiences.

In the last few years we have seen an explosion of real time data constantly generated by every individual, machine and organization. Real time data is everywhere: business transactions in operational systems, customer and partner interactions, third- party data services in the cloud, and IoT data generated by sensors and devices.

All this real time data creates new opportunities to build innovative applications such as fraud detection, personalized offers for customers, vaccine distribution, smart pricing, in-game analytics, predictive maintenance, content recommendations, connected cars and more.

Data streaming is the key

Data streaming is data that is continuously and/or incrementally flowing from a variety of sources to a destination to be processed and analyzed in near real-time. This unlocks a new world of use cases around real-time ETL, real-time analytics, real-time ML, and real-time operational applications that in turn enable faster decision making. To harness the power of real-time data, organizations must broadly embrace data streaming across multiple use cases and simplify adoption of technology to boost differentiation within their markets.

However many platforms do not provide a unified and simplified approach for streaming across all use cases. According to IDC, less than 25% of enterprises have adopted data streaming across the enterprise. Many organizations manage to successfully implement use cases leveraging data streaming pipelines for one off use cases, but struggle to make data streaming the norm vs the exception. So why is this?

Challenges adopting data streaming

In reality, data streaming is really hard for most organizations. This is for several reasons:

• Specialized API's and language skills: Data practitioners encounter barriers to adopting streaming skillsets because there are new languages, APIs and tools to learn.
• Operational complexity: To implement data streaming at scale, data teams need to integrate and manage streaming-specific tools and cloud services. They also have to manually build complex operational tooling to help these systems recover from failure, restart workloads without reprocessing data, optimize performance, scale the underlying infrastructure and so on.
• Incompatible governance models: Different governance and security models across real-time and historical data platforms makes it difficult to provide the right access to the right users or see the end to end data lineage, and meet compliance requirements.

How the Databricks Lakehouse Platform makes data streaming simple

The Databricks Lakehouse Platform overcomes these challenges by making data streaming incredibly simple. Enabling every organization to deliver real time analytics, machine learning and applications on one platform. This is for 3 main reasons:

• It enables all your data teams. With Databricks, data engineers, data scientists, and analysts can easily build streaming data workloads with the languages and tools they already know and the API's they already use.
• It simplifies development and operations. Databricks give you out of the box capabilities that automate much of the production aspects associated with building and maintaining real-time data pipelines.
• It offers a unified platform for streaming and batch data. Databricks helps you eliminate data silos, centralize your security and governance models for all your use cases across clouds.

Many of our customers, from enterprises to startups across the globe, love and trust Databricks. We have over 9,000 global customers across all industries building amazing solutions and delivering business impact with the lakehouse architecture. When it comes to data streaming, many of our customers such as AT&T, Walgreens, Columbia Sportswear, Edmunds, US Department of Transportation, Akamai, Kythera Labs and more moved to the lakehouse and have seen fantastic success.

The Databricks Lakehouse Platform is a preferred choice for many organizations. Here are the top three reasons why customers love data streaming on the Databricks Lakehouse Platform:

1. The ability to build streaming pipelines and applications faster

The Databricks Lakehouse Platform unlocks data streaming for the data teams you have in place today enabling you to build streaming data pipelines and real-time applications faster than ever before.

Analysts, analytics engineers, data scientists and data engineers can easily build streaming pipelines using the tools and languages they are already familiar with like SQL and Python and avoid learning new languages and API's or specialized streaming technology.

Delta Live Tables (DLT) turns SQL analysts into data engineers. Simple semantic extensions to SQL languages enable them to work with streaming data right away. For example using Delta Live Tables practitioners can add the word 'streaming' to a simple create table statement to create a streaming pipeline. Instead of low-level hand-coding of ETL logic, data engineers can build declarative pipelines – easily defining 'what' to do, not 'how' to do it. DLT automatically manages all the dependencies within the pipeline. This ensures all tables are populated correctly, continuously or on a set schedule.

Both DLT and Spark Structured Streaming provide unified API's for streaming and batch workloads so data engineers and developers can build real-time applications with minimal changes to existing code and they can also continue to work in the same notebooks and SQL editors they already use and avoid learning new tools and IDE's.

In technology and software, Statsig helps developers make better decisions by introducing end-to-end observability for each product update they launch. Using Databricks, Statsig can stream data faster, which means it ingests data faster, starts jobs earlier and lands jobs on time. Statsig's data pipelines ingest more than 10 billion events a day. Its systems are certified for the highest compliance standards in the industry to manage and secure data, and they serve those billions of user interactions at 99.9% availability with built-in redundancy.

"With the Databricks Lakehouse Platform, the actual streaming mechanics have been abstracted away … this has made ramping up on streaming so much simpler."

— Pablo Beltran, Software Engineer, Statsig

2. Simplified operations with automated tooling

Developing code fast is a significant benefit but you then have to put that code into production. Customers frequently tell us they spend a huge amount of time writing and maintaining code to support the operational aspects of their streaming data pipelines. In fact this is often the lion's share of the overall effort. With Databricks the burden of building and maintaining operational tooling is significantly reduced through automated capabilities that come right out of the box.

Products like Delta Live Tables automate the complex and time consuming aspects of building streaming data pipelines like the ability to automatically recover from failure, autoscaling the underlying compute infrastructure, optimizing performance and much more.

You also get fully comprehensive monitoring to understand the health and performance of every aspect of your data pipelines and the ability to set up rules to automatically test and maintain data quality in real-time. You can define data quality and integrity controls, and address data quality errors with flexible policies such as being able to alert on, drop, or quarantine bad data or even fail pipelines.

Developing and testing data pipelines to catch issues early without impacting production is really hard. Data engineering & analyst teams lack the tools to implement known CI/CD software-best practices for developing and testing code.

In manufacturing, Honeywell's Energy and Environmental Solutions division uses IoT sensors and other technologies to help businesses worldwide manage energy demand, reduce energy consumption and carbon emissions, optimize indoor air quality, and improve occupant well-being. Using Delta Live Tables on the Databricks Lakehouse Platform, Honeywell can now ingest billions of rows of sensor data into Delta Lake and automatically build SQL endpoints for real-time queries and multilayer insights into data at scale — helping Honeywell improve how it manages data and extract more value from it, both for itself and for its customers.

"Databricks helps us pull together many different data sources, do aggregations, and bring the significant amount of data we collect from our buildings under control so we can provide customers value."

— Dr. Chris Inkpen, Global Solutions Architect, Honeywell Energy and Environmental Solutions

3. Unified governance for real-time and historical data

Real-time data typically resides in message queues and pub/sub systems like Apache Kafka and is separate from historical data found in data warehouse. This creates governance challenges for data engineers and data administrators trying to manage access to all data holistically across the enterprise.

Unity Catalog solves this problem by providing a single data governance model for both real-time and historical data in one place. With Unity Catalog you can unify access and auditing for all your data across any cloud. It also offers the industry's first open data sharing protocol enabling you to securely share data and collaborate with all your lines of business and even external organizations across the world.

And data in Unity Catalog becomes instantly discoverable for everyone in your organization providing complete visibility for faster insights and automatically provides comprehensive lineage that provides visibility into how data flows for impact analysis, including how it was transformed and used for every single real-time use case.

In technology and software, Grammarly's trusted AI-powered communication assistance provides real-time suggestions to help individuals and teams write more confidently. By migrating to the Databricks Lakehouse Platform, Grammarly is now able to sustain a flexible, scalable and highly secure analytics platform that helps 30 million people and 50,000 teams worldwide write more effectively every day. Using the lakehouse architecture, data analysts within Grammarly now have a consolidated interface for analytics. To manage access control, enable end-to-end observability and monitor data quality, Grammarly relies on the data lineage capabilities within Unity Catalog. By consolidating data onto one unified platform, Grammarly has eliminated data silos.

"The ability to bring all these capabilities, data processing and analysis under the same platform using Databricks is extremely valuable. Doing everything from ETL and engineering to analytics and ML under the same umbrella removes barriers and makes it easy for everyone to work with the data and each other."

— Sergey Blanket, Head of Business Intelligence at Grammarly

Get started with data streaming on the lakehouse

The Databricks Lakehouse Platform dramatically simplifies data streaming to deliver real time analytics, machine learning and applications on one platform. The top three reasons why Databricks customers love data streaming on the lakehouse are the ability to build streaming pipelines and applications faster, simplified operations from automated tooling and unified governance for real time and historical data.

If you're looking to democratize data streaming across your organization and take your real-time decision-making to the next level for all your use cases, learn more about Data Streaming on the Lakehouse.

Watch our Data Engineering and Data Streaming virtual event if you want to see sessions, demos, best practices and more success stories that showcase how to build and run modern data pipelines to support real-time analytics, ML, and applications on the lakehouse.