Latest Databricks News, Funding, IPO, Services and More

Databricks is a data and AI company. It was founded in 2013 by the creators of lakehouse architecture and open-source projects like Apache Spark, Delta Lake, and MLflow.

More than 10,000 organizations, including over 60% of the Fortune 500, use the Databricks platform for their data analytics and AI needs.

Databricks SQL recently set a world record in the TPC-DS benchmark for data warehousing at 100TB, achieving 32,941,245 QphDS, which is 2.2x faster than the previous record held by Alibaba.

This performance showcases its superior price-performance ratio, being 12x better than Snowflake in similar setups.

The platform caters to a broad clientele that includes companies like Block, Comcast, Conde Nast, Rivian, and Shell. Databricks has developed a network of more than 1,200 global cloud and consulting partners.

There has been an upsurge in the use of vector databases for Generative AI applications, with the rate growing by 377% year-over-year as firms try to personalize AI models using their private data.

Databricks had declared raising $10 billion in the Series J funding round announced on December 18, 2024. This is the biggest venture capital round in history.

The funding was led by Thrive Capital and valued the company at $62 billion. Capital raised is expected to enable the growth of Databricks as it is expected to break the $3 billion revenue run rate and generate positive free cash flow by the fourth quarter of 2024.

Databricks is engaging its platform with new features targeted for generative AI applications. In early 2024, it launched a series of tools called Mosaic, with the capabilities to tailor and deploy AI models.

The company also positioned itself as a leader in building AI solutions by introducing the DBRX open-source foundation model, which is efficient and outperforms other models on numerous tasks.

Databricks Announces $10 Billion Funding

Databricks has announced a new funding milestone, raising $10 billion in its Series J funding round, which values the company at around $62 billion.

This funding round is one of the biggest in venture capital history and has already completed $8.6 billion.

The funding round is led by Thrive Capital, with major contributions from investors like Andreessen Horowitz, DST Global, GIC, Insight Partners, and others.

Existing investors like the Ontario Teachers’ Pension Plan also participated, along with new investors like ICONIQ Growth and Wellington Management.

With over $3 billion projected annual revenue at the end of January 2025. Revenue from more than 60 percent year-over-year growth have resulted primarily from rising demand for AI solutions.

More than 10,000 clients count Databricks. Companies on the list of the Fortune 500 include names that are represented among those 10,000.

Databricks CEO Ali Ghodsi About IPO

Databricks CEO Ali Ghodsi recently spelled out his reasons for postponing the company’s IPO at least until 2025, even though the firm has just raised $10 billion in its latest funding round, which has placed the company with a valuation of $62 billion.

Ghodsi mentioned that 2024 is an election year, and thus, there are risks from the interest rates and inflation. He said, “It’s dumb to IPO this year.”

There was huge interest in the latest funding round. According to Ghodsi, demand from investors came at $19 billion while their target was just $3 billion to $4 billion. Due to the level of interest, the prices went up during fundraising.

The Series J funding round will also be used to enable early employees to cash out as the company continues growing.

Ghodsi said that he is concerned by the current “AI bubble,” where many startups are seeing inflated valuations without products or innovation. He wants to avoid rushing into an IPO before this bubble might pop.

While Ghodsi has not closed the door on an IPO in 2025, he admitted that it may spill over into 2026 as well.

What are Databricks Used for?

Databricks is a platform used for data processing, analytics and machine learning. It integrates various functionalities to help organizations manage and analyze their data efficiently.

Databricks excels in extracting, transforming, and loading from different sources, allowing them to clean and prepare large volumes of data for analysis. It would be suitable for both types of data streams: batch and real time.

The platform is a strong data warehousing solution that lets businesses store and analyze both structured and semi-structured data.

Databricks supports complex SQL queries and is therefore effective in generating business intelligence reports and performing analytics in detail.

Databricks offers a collaborative environment for building, training, and deploying machine learning models. It supports popular libraries like TensorFlow, PyTorch, and scikit-learn.

Organizations can use Databricks for real-time analytics by processing streaming data from many sources. This is important for applications like fraud detection in finance or monitoring network traffic for cybersecurity.

Databricks provides tools for interactive data exploration, and users can analyze data using SQL, Python, R, or Scala. Its notebooks help users create visualizations and dashboards that help in identifying trends and patterns in the data.

The platform fosters collaboration between data scientists, engineers, and analysts through a common working platform, allowing them to work upon projects together, share insights, and streamline workflows.

By building on the machine learning capabilities of Databricks, predictive analytics can also be used for applications from retail to healthcare. With the prediction based on historical data trends in organizations.

With Databricks, businesses can examine customer behavior and preferences in order to strengthen marketing strategy and increase customer interaction through a personalized experience.

Databricks improves the management of an organization’s supply chain by analyzing its logistics data, inventory levels, and patterns of demand for optimizing processes.

The platform supports better cybersecurity as it analyses network activity in real-time to identify threats and anomalies.

Is Databricks Azure or AWS?

Databricks works on different cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP).

1. Azure Databricks: This is a first-party service co-created by Databricks and Microsoft. It is aimed at having an integration with the security and data services of Azure. It allows for unified analytics, where one can build and deploy the data solutions in the Azure environment.

2. Databricks on AWS: This version allows users to leverage AWS infrastructure, enabling them to manage data stored in Amazon S3 while utilizing Databricks’ capabilities for data processing and analytics.

3. Databricks on Google Cloud: Similar to its counterparts, this service integrates with Google Cloud’s data services, allowing users to build and manage data applications using Databricks on GCP.

Is Databricks an ETL Tool?

Yes, Databricks could be called an ETL tool because it provides all the necessities to build up and maintain ETL, or even ELT-Extract, Load, Transform data pipelines.

Do Databricks Require Coding?

Yes, Databricks do need coding as well because this is primarily data processing, analytics, or machine learning that always happens with the help of writing a code.

Databricks supports multiple programming languages: Python, Scala, R, Java, and SQL. Users may choose any language that fits their requirements for data manipulation and analysis.

Users interact with Databricks through notebooks where they write code for data transformation, creating visualizations, and building machine learning models. The notebooks feature autocomplete and debugging tools for a smoother coding experience.

Python, especially through the PySpark API, is widely used for data processing tasks in Databricks. Although SQL can be used to query data, more complex transformations often require coding in PySpark.

Users can automate ETL processes and workflows using code within Databricks. This involves scripting to define how data should be extracted, transformed, and loaded into various systems.

Databricks notebooks facilitate collaboration in the development of code between many users. Also, it supports version control using systems such as Git. Therefore, coding practices have to be understood.

Who Owns Databricks?

Databricks is owned by a combination of its co-founders, venture capital firms, and investors.

Co-Founders

• Ali Ghodsi – Co-Founder and CEO
• Matei Zaharia – Co-Founder and CTO
• Ion Stoica – Co-Founder
• Andy Konwinski
• Reynold Xin
• Patrick Wendell

Major Investors

• Andreessen Horowitz
• Coatue Management
• New Enterprise Associates
• T. Rowe Price
• BlackRock

Partnerships

• Microsoft
• Google
• Amazon

Is Databricks Saas or PaaS?

Databricks is classified as a PaaS and also SaaS.

Databricks, being a PaaS, provides developers with an integrated development environment (IDE) that lets them build, manage, and deploy big data applications without having to worry about underlying infrastructure.

Tools for data ingestion, transformation, and analysis are also provided so that users can write code in various programming languages like Python and Scala using its notebooks.

On the SaaS side, Databricks delivers its services over the internet, so the organizations can access its analytics capabilities without needing to manage software installations or infrastructure upgrades.

Users can subscribe to Databricks’ services and use its features for data processing and analytics without worrying about hardware management.

What Type of SQL is Databricks?

Databricks uses its own version of SQL called Databricks SQL. This SQL syntax is optimized to work efficiently with the Databricks platform, especially for analytics and processing of big data as well as machine learning applications.

Databricks SQL is built on top of Apache Spark, and users can take advantage of the Spark distributed computing model for the efficient processing and querying of data.

The SQL dialect is optimized for use with Delta Lake, which is a storage layer bringing ACID transactions to Apache Spark and big data workloads. It improves the data reliability and performance.

Databricks SQL supports the vast majority of data types: numeric types (like INT, BIGINT, FLOAT), string types (like STRING), boolean types, date/time types, binary types, and complex types like ARRAY and MAP.

Users can run standard SQL commands such as SELECT, INSERT, UPDATE, and DELETE. It also supports advanced analytics functions and window functions that are necessary for data analysis tasks.

The platform offers an interactive notebook interface where users can write queries in Databricks SQL along with visualizations and markdown documentation

Databricks Community Edition

Databricks Community Edition is a free version of the Databricks platform. It is used to let users learn and experiment with big data processing using Apache Spark.

The Community Edition provides access that never expires, making it very ideal for people who would want to gain hands-on experience about data analytics and machine learning.

A micro-cluster with 15GB of storage is accessible. It allows running small-scale applications and experiments.

It features a collaborative notebook environment where users can write code in multiple languages-Python, R, Scala, SQL-and create visualizations.

It supports JDBC/ODBC integrations for business intelligence analysis. Through this edition, users can easily connect to different data sources.

Signing up for Databricks Community Edition is easy: the only thing needed is a verified email address. Contrary to the free trial of the full version, there is no need to set up a separate cloud account or extra resources.

Also Read: Latest AI Startup News, Funding, Companies, Jobs and More – 2024

Databricks Careers

Databricks is looking to fill many positions within various functions, such as software engineering, data engineering, product management, sales, marketing, and customer support. These include Software Engineer – Backend, AI/ML Developer, and Technical Curriculum Developer.

Databricks is hiring in multiple countries, such as the United States (San Francisco, Mountain View, Seattle), Europe (Amsterdam, Berlin), and the Middle East (Dubai, Riyadh). They also have remote opportunities around the world.

Job Opportunities

• Software Engineer (Backend)
• AI/ML Developer
• Data Engineer
• Technical Curriculum Developer
• Commercial Account Executive
• Recruiting Coordinator (Contract)

Databricks is one of the fastest-growing enterprise software companies, with over 7,000 employees worldwide.

Those interested can search for jobs on LinkedIn and Indeed or check out the Databricks careers page. The hiring process involves a resume submission followed by interviews where technical skills and cultural fit are evaluated.

Databricks Certifications

Databricks offers a range of certifications designed to validate skills in data engineering, data analysis and machine learning on its platform. 

Available Certifications

1. Databricks Certified Data Analyst Associate
2. Databricks Certified Data Engineer Associate
3. Databricks Certified Data Engineer Professional
4. Databricks Certified Machine Learning Associate
5. Databricks Certified Machine Learning Professional
6. Databricks Certified Associate Developer for Apache Spark
7. Databricks Certified Hadoop Migration Architect

Certification Process

1. Select a Certification: Choose a certification that aligns with your career goals.
2. Prepare for the Exam: Utilize available training resources, including free on-demand courses offered by Databricks.
3. Register for the Exam: Sign up for the exam through the Databricks certification portal.
4. Take the Exam: Complete the exam to validate your skills.
5. Share Your Achievement: You can post your passing certificate on sites like LinkedIn once you pass.

Databricks vs Snowflake

Databricks Pricing

Databricks and Cloud Integration

Cloud Providers

Amazon Web Services (AWS): Databricks natively integrates with all AWS services such as S3, EC2, and Redshift. It makes it possible for users to manage and analyze data in AWS in the best way possible.

Microsoft Azure: Azure Databricks is a first-party service, meaning that there is a deep integration of Databricks with the security and data services in Azure.

Google Cloud Platform (GCP): Databricks on GCP provides tight integration with Google Cloud Storage, BigQuery, and the Google Cloud AI Platform, allowing users to consolidate their analytics applications on a single platform.

Alibaba Cloud: Databricks also supports Alibaba Cloud, extending its capabilities to users in that ecosystem.

Data Connectivity

Data Sources: Users can connect to various data formats (CSV, JSON, Parquet) and storage providers (Amazon S3, Google BigQuery, Snowflake) for reading and writing data.

BI Tools Integration: Databricks integrates with popular business intelligence tools such as Power BI and Tableau.

ETL Tools: It supports integration with ETL/ELT tools like dbt, Azure Data Factory, and orchestration tools like Apache Airflow.

Managed Services

Infrastructure Management: Databricks manages underlying infrastructure on behalf of users, who can focus on building and deploying analytics applications without worrying about hardware management.

Security Features: This platform leverages the security features of the underlying cloud provider, ensuring compliance with various standards (e.g. SOC 2 Type II, ISO 27001).

Partner Connect: This feature helps users discover and integrate validated third-party solutions quickly within the Databricks environment.

Databricks Ecosystem

Open Source Roots

Databricks was founded by the original creators of Apache Spark, an open-source distributed computing framework.

Databricks continues to support and contribute to open-source projects. For example, it collaborated on Delta Lake, an open-source storage layer that improves data reliability and performance in big data processing.

Recently, Databricks launched DBRX, the open-source large language model designed for the development of custom AI models.

Partner Integrations

Databricks partners with major cloud providers, including AWS, Microsoft Azure, and Google Cloud, to provide the services.

The platform has been integrated with multiple third-party tools for data visualization, ETL processes, and machine learning, including Tableau, Power BI, dbt, Azure Data Factory, TensorFlow, and PyTorch. This will make it easy for users to reuse tools within their workflow.

Mistral AI partners can easily make use of other open-source models through this interface.

Latest Databricks News

Databricks has managed to raise $10 billion in its latest funding round, boosting its valuation to $62 billion. The company reported a revenue run rate of $3 billion and aims to achieve positive cash flow by the end of January 2025.

Already securing $8.6 billion in the funding round, the investment has been co-led by Thrive Capital and existing investors including Andreessen Horowitz and DST Global.

Databricks has seen more than 60% year-over-year growth, with over 500 customers bringing in more than $1 million annually. Its SQL data warehousing product has a revenue run rate of $600 million, up 150% from last year.

The company outlined plans for an international growth including new regional hubs in London and Singapore, as well as expansion in Latin America and the Middle East.

Analysts believe that Databricks may be exploring an initial public offering, or IPO, in mid-2025, as the number of tech IPOs is going up.

Databricks has raised $10 billion in its Series J funding round, with $8.6 billion completed to date. The funding values the company at $62 billion.

The company is expected to surpass a $3 billion annual revenue run rate by the end of its fourth quarter, which concludes on January 31, 2025.

Databricks has more than 500 customers who each have over $1 million in annual revenue.

The company had revenue growth of more than 60% year-over-year in the third quarter of 2024.

The company’s Databricks SQL product has a revenue run rate of $600 million, which is an increase of over 150% from the previous year.

Databricks is expecting to achieve positive free cash flow for the first time in its upcoming quarter.

The company continues to maintain non-GAAP subscription gross margins above 80%.

Databricks has done massive acquisitions, including MosaicML for $1.3 billion and others such as Okera and Arcion.

Also Read: Top AI Applications in Healthcare Industry 2024