Databricks was founded out of the UC Berkeley AMPLab by the team that created Apache Spark. We’ve been working for the past six years on cutting-edge systems to extract value from Big Data. We believe that Big Data is a huge opportunity that is still largely untapped, and we’re working to revolutionize what you can do with it.
Open Source Commitment
Apache Spark is 100% open source, hosted at the vendor-independent Apache Software Foundation. At Databricks, we are fully committed to maintaining this open development model. We believe that no computing platform will win in the Big Data space unless it is fully open. Spark has one of the largest open source communities in Big Data, with over 1000 contributors from 250+ organizations. Databricks works within the open source community to maintain this momentum.
Solutions with Databricks
Advertising and Marketing Technology
The growth of digital advertising and marketing data has created a plethora of opportunities to optimize campaign performance and advertising spend across direct advertising and auctions. Databricks helps you manage the traffic jam of data caused by multiple sources of data such as ad inventory, web traffic, click logs, CRM, and behavioral data to uncover insights that improve audience targeting, pricing strategies, and conversion rates — increasing campaign ROI and creating new revenue opportunities.
Click stream Analytics: Improve the user experience to drive revenue by analyzing customer behavior, optimizing company websites, and offering better insight into customer segments.
Audience Targeting: Analyze customer profile data to deliver personalized ad types, retargeting, and targeted ad placements that result in higher engagement, return on ad spend (ROAS), and lifetime value (LTV).
Automate Campaign Decisions: Optimizing campaign decision-making is a growing trend in digital advertising. Access and explore campaign performance data across your team to reveal hidden insights about ad performance and automatically optimize campaigns.
Internet of Things (IoT)
The data generated from ever-increasing network sensors brings unprecedented visibility into previously opaque systems and processes. The key is finding actionable insights in this torrent of information. Databricks allows IOT companies to analyze high-velocity sensor and time-series data in real-time, allowing them to harness the true value of an interconnected world to deliver improved customer experiences, operational efficiencies, and new revenue opportunities.
Connected Car: It effortlessly ingest and process streaming data to deliver real-time vehicle health and telematics for predictive maintenance, navigation, communication, and security.
Logistics: Leverage sensor data to enable smarter supply chain management to achieve improved operational efficiency across fleet performance and warehouse inventory tracking.
Health and Fitness: Iterate rapidly to develop new fitness features and products based on information hidden in the sensor data from wearable technology such as smart watches.
From banks to investment firms, the financial services industry can benefit from access to advanced data analytics to harness insights from a host of datasets such as market data, CRM data, articles, and more. Leveraging data science helps minimize operational costs while improving the ability to execute on common financial services use cases such as more accurate financial forecasting, analyzing customers to optimize marketing spend and reduce churn, assessing portfolio risk, mitigating fraudulent behavior, and making data-driven investment recommendations.
Financial Modeling: Leveraging various data points such as market data, news and sentiment data, and transactional data for more accurate forecasting and market trend analysis.
Risk and Fraud Detection: Detect and prevent fraud (e.g. money laundering, credit card fraud) by leveraging advanced analytics to predict anomalies in real time.
Financial Product Recommendations: Analyze millions of structured and unstructured data points across the market and customer sentiments to recommend the right product or investment strategy that delivers optimal returns to you and your customers.
Media and Entertainment
To successfully compete for the fleeting attention of their customer base, content producers and publishers today need to personalize content. To tailor your offering based on the vast number of data sources ranging from event data (e.g. viewing behavior, search history) to social media and third party sources like Nielsen, you need a data platform that can help you analyze data holistically and develop advanced predictive models that increase engagement and customer lifetime value (LTV).
Product Recommendations: Use customer behavior data and profile information to predict next best offers and provide a more personalized experience to drive customer engagement and LTV.
Sentiment Analysis: Analyze how social, advertising, competitor moves, product launches or news stories affect the brand.
Pricing Optimization: Analyze demand and inventory data such as segmentation, attribution, and cost to determine prices and set discounts.
Behind the Venture
Ali Ghodsi, Co-founder and Chief Executive Officer: Ali is
the CEO and co-founder of Databricks, responsible for the growth and international expansion of the company. He previously served as the VP of Engineering and Product Management before taking the role of CEO in January 2016. In addition to his work at Databricks, Ali serves as an adjunct professor at UC Berkeley and is on the board at UC Berkeley’s RiseLab.
Ali was one of the creators of open source project, Apache Spark, and ideas from his academic research in the areas of resource management and scheduling and data caching have been applied to Apache Mesos and Apache Hadoop. Ali received his MBA from Mid-Sweden University in 2003 and PhD from KTH/Royal Institute of Technology in Swedenin 2006in the area of Distributed Computing.