Why Startups Build on Databricks

In the 2010s, cloud infrastructures enabled a generation of startups to build and scale their businesses. In this decade, cloud infrastructure is table stakes, and the differentiation of a startup product comes from data, analytics, and AI.

Today's startups have to build on a scalable data platform. As an entrepreneur building a product, choosing the right data platform can be the difference between success and failure.

Your product needs a data platform that can:

• Address all data, analytics, and AI use cases in one platform
• Fully manage your data infrastructure to maximize your speed to product
• Prepare your product for growth with cost-effective scalability and performance
• Offer infrastructure flexibility with open source and multi-cloud

Hundreds of successful startups - such as Abnormal Security, People.ai, and YipitData - have built their products on the Databricks Lakehouse with great success. Lets examine some of the factors that drove their data platform decision.

Data without compromise: Executing on all your data, analytics, and AI needs

A modern data-driven application likely involves multiple data types and use cases. For example, if you build a cybersecurity application, you'll need streaming software to read and process semi-structured logs. You may display a dashboard driven by structured data loaded in batch. Most databases specialize in one or two use cases. This forces product builders to compromise and build multiple siloed data pipelines.

A lakehouse architecture is built to handle BI and AI, structured and unstructured data, batch and streaming - all equally well. It simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning.

Abnormal Security, a leading email security vendor, built their product on the Databricks Lakehouse Platform and use it across diverse use cases. With infrastructure no longer a challenge, they are now able to ingest data directly from S3 and query it in near real-time for both streaming and batch operations. Data flows from Kinesis Firehose into Delta Lake, making threat signals data instantly available to data scientists. With Databricks SQL, data scientists are then able to create visualizations using rich dashboards to drive product decisions and improve detection efficacy.

"Databricks Lakehouse enables us to organize and leverage all our data at scale, to power analytics in an effort to detect and block all forms of email attacks for our customers"

— Sanny Liao, Head of Data Science at Abnormal Security

Speed to product: let your team focus on building the core application

In our conversations with startup founders, we find they share the same top priority: speed-to-product. Getting the product to market faster can make the difference between a startup winning or losing and surviving to the next milestone or going bust. With that in mind, developer productivity is of utmost importance. Founders need developers to focus on what you hired them to do - building the core product.

Many developers pride themselves on their versatility. But do you want your most valuable resource wasting time on managing Spark or troubleshooting ETL? The rational answer is almost always "no."

The Databricks Lakehouse Platform is fully managed and governed, enabling your team to focus on getting your product to market as quickly as possible.

Building on the Lakehouse enabled People.ai to improve developer productivity significantly. Companies across multiple industries choose People.ai to capture customer contacts, activity and engagement to drive actionable insights across all Enterprise revenue creation. People.ai found that management of data pipeline infrastructure was resulting in significant DevOps overhead and chose to build on Databricks.

Databricks reduced the time required for DevOps with end-to-end workflows built on Databricks notebooks. Less time spent on managing Spark infrastructure enabled them to focus on addressing customer and market demands by migrating new use cases seamlessly into production. Building on Databricks enabled 20%-30% reduction in DevOps costs.

"We were looking for a leader to partner with on analytics infrastructure. With Databricks, we can focus our time and resources on innovating new solutions that drive our business."

— John Wulf, Principal Engineer at People.ai

Prepare for growth: cost-effective performance at any scale

As a startup founder, it is reasonable to consider product scalability as a first-world problem to be considered down the road. However, growth comes unexpectedly, and the product must be ready. The data infrastructure needs to perform cost-effectively at scale.

Databricks Lakehouse Platform scales with your high-growth product. With better cost-efficiency than competing solutions, it can perform at any scale, from gigabytes to petabytes. Using the next-generation vectorized query engine Photon, the Lakehouse provides up to 12x better price/performance than other cloud data warehouses.

Look at how YipitData built on Databricks for scale and cost optimization. YipitData is in the business of providing data-driven insights to the world's largest hedge funds and corporations to help them gain a real competitive edge and provide better service to their customers. Each month, they make billions of requests collecting data from hundreds of websites.

By leveraging Databricks, YipitData's data team has reduced data processing time by up to 90 percent. Additionally, by moving to Databricks on AWS, YipitData has reduced database expenses by almost 60%.

"With Databricks, we're innovating faster than ever before across our data engineering and analyst functions, and paying less in database expenses every year."

— Steve Pulec, CTO at YipitData

Maintain infrastructure flexibility with open source and multi-cloud

It is important to build flexibility into your data architecture from the outset. Future data needs will evolve. Don't lock your product data into a vendor you cannot change. As your startup grows, you will inevitably have to consider expanding beyond a single cloud provider and evolve your data platform to support your new use cases.

Databricks Lakehouse is based on the fully open source Delta Lake project and runs on all three cloud platforms: AWS, GCP, and Azure. If you build your product on Databricks, you will always have the choice to bring your data to other cloud or data platforms.

Build your startup on the Databricks Lakehouse

The Databricks Lakehouse Platform addresses all data, analytics, and AI use cases in one platform; accelerates your speed to product; prepares your product for growth, and offers infrastructure flexibility for the long term. In this day and age, startups must innovate with data for their products to stand out. Build your startup on Databricks Lakehouse Platform to stay ahead of the competition. Learn more about Startup solutions on Databricks

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