Today, we’re celebrating an important milestone for the Spark project — it’s now been five years since Spark was first open sourced. When we first decided to release our research code at UC Berkeley, none of us knew how far Spark would make it, but we believed we had built some really neat technology that we wanted to share with the world. In the five years since, we’ve been simply awed by the numerous contributors and users that have made Spark the leading-edge computing framework it is today. Indeed, to our knowledge, Spark has now become the most active open source project in big data (looking at either contributors per month or commits per month). In addition to contributors, it has built up an array of hundreds of production use cases from batch analytics to stream processing.
Apache® Spark™ News
In this article, we are going to discuss these improvements in more detail.
With Spark 1.3, MLlib now supports Latent Dirichlet Allocation (LDA), one of the most successful topic models. LDA is also the first MLlib algorithm built upon GraphX. In this blog post, we provide an overview of LDA and its use cases, and we explain how GraphX was a natural choice for implementation.
The Spark 1.3 release represents a major milestone for Spark SQL. In addition to several major features, we are very excited to announce that the project has officially graduated from Alpha, after being introduced only a little under a year ago. In this blog post we will discuss exactly what this step means for compatibility moving forward, as well as highlight some of the major features of the release.
This is a guest blog from Matt Kalan, a Senior Solution Architect at MongoDB
Today I’m excited to announce the general availability of Spark 1.3! Spark 1.3 introduces the widely anticipated DataFrame API, an evolution of Spark’s RDD abstraction designed to make crunching large datasets simple and fast. Spark 1.3 also boasts a large number of improvements across the stack, from Streaming, to ML, to SQL. The release has been posted today on the Apache Spark website.
Today, we are excited to announce a new DataFrame API designed to make big data processing even easier for a wider audience.
2014 has been a year of tremendous growth for Apache Spark. It became the most active open source project in the Big Data ecosystem with over 400 contributors, and was adopted by many platform vendors – including all of the major Hadoop distributors. Through our ecosystem of products, partners, and training at Databricks, we also saw over 200 enterprises deploying Spark in production.
Recently Infoworld unveiled the 2015 Technology of the Year Award winners, which range from open source software to stellar consumer technologies like the iPhone. Being the creators and driving force behind Spark, Databricks is thrilled to see Spark in their ranks. In fact, we built our flagship product, Databricks Cloud, on top of Spark with the ambition to revolutionize big data processing in ways similar to how iPhone revolutionized the mobile experience.
In this blog post, we introduce Spark SQL’s JSON support, a feature we have been working on at Databricks to make it dramatically easier to query and create JSON data in Spark. With the prevalence of web and mobile applications, JSON has become the de-facto interchange format for web service API’s as well as long-term storage. With existing tools, users often engineer complex pipelines to read and write JSON data sets within analytical systems. Spark SQL’s JSON support, released in version 1.1 and enhanced in Spark 1.2, vastly simplifies the end-to-end-experience of working with JSON data.