Databricks announced that the company will contribute all features and enhancements it has made to Delta Lake to the Linux Foundation and open source all Delta Lake APIs as part of the Delta Lake 2.0 release. In addition, the company announced MLflow 2.0, which includes MLflow Pipelines, a new feature to accelerate and simplify ML model deployments. Finally, the company introduced Spark Connect, to enable the use of Spark on virtually any device, and Project Lightspeed, a next generation Spark Structured Streaming engine for data streaming on the lakehouse.
StreamSets Launches StreamSets Transformer
StreamSets, Inc., provider of the DataOps platform for modern data integration, released StreamSets® Transformer, a simple-to-use, drag-and-drop UI tool to create native Apache Spark applications. Designed for a wide range of users — even those without specialized skills — StreamSets Transformer enables the creation of pipelines for performing ETL, stream processing and machine-learning operations. Now, data engineers, scientists, architects and operators gain deep visibility into the execution of Apache Spark while broadening usage across the business.
State of the Art Natural Language Processing at Scale
The two part presentation below from the Spark+AI Summit 2018 is a deep dive into key design choices made in the NLP library for Apache Spark. The library natively extends the Spark ML pipeline API’s which enables zero-copy, distributed, combined NLP, ML & DL pipelines, leveraging all of Spark’s built-in optimizations.
Databricks Partners with RStudio To Increase Productivity of Data Science Teams
Databricks, a leader in unified analytics and founded by the original creators of Apache Spark™, announced a partnership with RStudio, providers of a free and open-source integrated development environment for R, to increase the productivity of data science teams. The partnership will allow the two companies to seamlessly integrate Databricks’ Unified Analytics Platform with the RStudio Server, simplifying R programming on big data.
Apache Spark 2.0: A Deep Dive Into Structured Streaming
In this talk, Tathagata Das takes a deep dive into the concepts and the API and show how this simplifies building complex “Continuous Applications”. Tathagata is an Apache Spark Committer and a member of the PMC. He’s the lead developer behind Spark Streaming, and is currently employed at Databricks.
Top 5 Mistakes When Writing Spark Applications
In the presentation below from Spark Summit 2016, Mark Grover goes over the top 5 things that he’s seen in the field that prevent people from getting the most out of their Spark clusters. When some of these issues are addressed, it is not uncommon to see the same job running 10x or 100x faster with the same clusters, the same data, just a different approach.
The Data Scientist’s Guide to Apache Spark
Looking to dive deeper into the more cutting edge machine learning use cases in Apache Spark? To successfully use Spark’s advanced analytics capabilities including large scale machine learning and graph analysis, check out The Data Scientist’s Guide to Apache Spark, from our friends over at Databricks.
The Data Scientist’s Guide to Apache Spark™
For data scientists looking to apply Apache Spark’s advanced analytics techniques and deep learning models at scale, Databricks is happy to provide The Data Scientist’s Guide to Apache Spark. Download this eBook to: Learn the fundamentals of advanced analytics and receive a crash course in machine learning. Get a deep dive on MLlib, the primary […]
Databricks Launches Delta To Combine the Best of Data Lakes, Data Warehouses and Streaming Systems
Databricks, provider of the leading Unified Analytics Platform and founded by the team who created Apache Spark™, announced Databricks Delta, the first unified data management system that provides the scale and cost-efficiency of a data lake, the query performance of a data warehouse, and the low latency of a streaming ingest system. Databricks Delta, a […]
Apache Spark Expands With Cypher, Neo4j’s ‘SQL For Graphs,’ Adds Declarative Graph Querying
Neo4j, a leader in connected data, announced that it has released the preview version of Cypher for Apache Spark (CAPS) language toolkit. This combination allows big data analysts to incorporate graphs and graph algorithms in their work, which will dramatically broaden how they reveal connections in their data.