Databricks @ Strata SJ
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Databricks is going to Strata San Jose! This presentation introduces our flagship product, Databricks Cloud. More details: Databricks Cloud combines the power of Spark with a zero-management hosted platform and an initial set of applications built around common workflows to simplify the pain of provisioning a Spark cluster, exploring data, and building data products. Spark is a unified processing engine that eliminates the need to stitch together a disjointed set of tools, and provides support for interactive queries (Spark SQL), streaming data (Spark Streaming), machine learning (MLlib) and graph computation (GraphX) in a single common API across the entire pipeline. Additionally, Databricks Cloud reaps the benefit of the rapid pace of innovation in Spark, the fastest growing Apache project with over 400 contributors
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Databricks @ Strata SJ
- 1. Making Big Data Simple with Databricks Cloud February 2015
- 2. Your company starts a Big Data initiative You have data, you have questions, now what?
- 3. You have to navigate your data pipeline Get a cluster up and running Build a production pipeline Import and explore data
- 4. Problem: the journey is complex and costly Expensive to build and hard to manage Disparate and difficult tools Months of re- engineering to deploy Get a cluster up and running Build a production pipeline Import and explore data
- 5. The Solution: Databricks Cloud A unified, cloud-hosted data platform powered by Spark
- 6. From Challenges to Solutions Clusters are expensive to build and hard to manage Tools are disparate and difficult to integrate and use Long deployment cycles Hosted clusters in the Cloud Unified Interactive Workspace Instant deployment
- 7. What is Databricks Cloud Managed Spark Clusters in the Cloud Notebook Environment 3rd Party Applications Production Pipeline Scheduler
- 8. Managed Spark Clusters in the Cloud No lead time No DevOps Instant Spark clusters The best place to run Spark No expensive hardware
- 9. Explore and visualize data interactively Program in Python, SQL, and Scala Collaborate on-line in real-time Notebook environment
- 10. WYSIWYG builder Interactive plots One-click publishing Custom dashboards
- 11. Schedule production Spark jobs without complicated scripts Production pipeline scheduler
- 12. Real-time Decisions Learn, work and collaborate in a single, easy to use environment Zero Management Focus on your data, not operationsInstant Results From insights to products in just hours Open Platform Use your favorite tools to leverage a growing ecosystem. Why Databricks Cloud
- 13. About Databricks Founded by the creators of Spark in 2013 Largest organization contributing to Spark End-to-end hosted platform, Databricks Cloud
- 14. Get Started Now! Sign up for Databricks Cloud: https://databricks.com/registration
Notas del editor
- We do this by addressing every one of the three challenges: 1) To alleviate the need to set up and maintain clusters we provide a hosted solution, which makes it very easy to instantiate and manage clusters. 2) To obviate the need to deal with a zoo of tools, we are leveraging Apache Spark which integrates the functionalities of many of the existing big data tools and systems. 3) Finally, to dramatically simplify the use of these tools, we are introducing a web Workspace which allows users to interactively query and visualize the data.
- Notebooks provide interactive query processing and visualization, and support on-line collaboration, thus allowing multiple users conduct jointly data explorations. Currently, notebooks allows users to query and analyze data using python, sql, and scala.
- Notebooks provide interactive query processing and visualization, and support on-line collaboration, thus allowing multiple users conduct jointly data explorations. Currently, notebooks allows users to query and analyze data using python, sql, and scala.
- Once you create one or more notebooks, you can take the most interesting results from these notebooks and create sophisticated dashboards. You can do this through a simple and intuitive dashboard builder, and then publish a dashboard with the click of a click. The dashboards are interactive in that every plot can depend on one or more variables. When these variables are updated, the query behind each plot is automatically re-executed, and the plot is regenerated.
- Finally, the Workspace includes a job launcher that enables you to run arbitrary spark jobs programmatically. For example you can automatically schedule jobs to run when their input changes or periodically.