Welcoming Redash to Databricks

栏目: IT技术 · 发布时间: 4年前

内容简介:This morning at Spark and AI Summit, we announced that Databricks has acquired Redash, the company behind the popular open source project of the same name. With this acquisition, Redash joins Apache Spark, Delta Lake, and MLflow to create a larger and more

This morning at Spark and AI Summit, we announced that Databricks has acquired Redash, the company behind the popular open source project of the same name. With this acquisition, Redash joins Apache Spark, Delta Lake, and MLflow to create a larger and more thriving open source system to give data teams best-in-class tools. I would like to take this opportunity to send a warm public welcome to the Redash team and the open source community, and share with you our thinking behind the acquisition.

Welcoming Redash to Databricks

As part of the announcement, we also shared our plan for a hosted version of Redash that will be fully integrated into the Databricks platform to create a rich visualization and dashboarding experience. The integrated experience is currently available in private preview, and you can sign up for the private preview waitlist to be the first to try it out.

What is Redash?

Redash is a collaborative visualization and dashboarding platform designed to enable anyone, regardless of their level of technical sophistication, to share insights within and across teams. SQL users leverage Redash to explore, query, visualize, and share data from any data sources. Their work in turn enables anybody in their organization to use the data. Every day, millions of users at thousands of organizations around the world use Redash to develop insights and make data-driven decisions.

Redash includes the following features:

  1. Query editor: Quickly compose SQL and NoSQL queries with a schema browser and auto-complete.
  2. Visualization and dashboards: Create beautiful visualizations with drag and drop, and combine them into a single dashboard.
  3. Sharing: Collaborate easily by sharing visualizations and their associated queries, enabling peer review of reports and queries.
  4. Schedule refreshes: Automatically update your charts and dashboards at regular intervals you define.
  5. Alerts: Define conditions and be alerted instantly when your data changes.
  6. REST API: Everything that can be done in the UI is also available through REST API.
  7. Broad support for data sources: Extensible data source API with native support for a long list of common SQL, NoSQL databases and platforms.

Easily run SQL queries against Delta Lake or any other data sources

Welcoming Redash to Databricks

Quickly turn results into visualizations

Welcoming Redash to Databricks

Share live dashboards with collaborators

Welcoming Redash to Databricks

Redash and Databricks

We first heard about Redash a few years ago through some of our early customers. As time progressed, more and more of them asked us to improve the integration between Databricks and Redash. Earlier this year, we invited Arik Fraimovich, Redash’s founder and CEO, to visit Databricks to discuss how we can collaborate and make data easier to consume.

Within the first hour of meeting Arik, it became very obvious to us that the two companies have so much in common. Our acquisition of Redash was driven not only by the great community and product they’ve developed, but also the same core values we share. Both our organizations have sought to make it easy for data practitioners to collaborate around data, and democratize its access for all teams. Most importantly though, has been the alignment of our cultures to help data teams solve the world’s toughest problems with open technologies.

We’re excited to welcome Arik and the Redash team to Databricks, and to further develop Redash together and deliver a seamless and more powerful experience for our customers and the broader open source communities.

Manage, use, and now consume data with a single platform

The new integrated Redash service gives SQL analysts a familiar home in Databricks, and gives data scientists and data engineers a place to easily query and visualize data in Delta Lakes and other data sources.

It seamlessly integrates with the existing Databricks platform: this service will be available in all data centers Databricks operate in; identity management and data governance are unified without additional configuration; SQL endpoints are automatically populated in Redash; catalogs and metadata are shared by two products.

And most importantly, for customers who are already using the two products: shift+enter (the keyboard shortcut to execute a query in Databricks) will also now function the same way in Redash!

Creating an open, unified platform for all data teams

Our vision at Databricks has been to deliver a unified data analytics platform that can help every data team throughout a company solve the world’s toughest problems — including data analysts, data engineers, data scientists, and machine learning engineers. By giving each team the tools they need for their own work, while also having a shared platform where they can collaborate, every data team can be successful together. This ultimately helps to deliver on the promise of thelakehouse data management paradigm, by combining the best capabilities of data lakes and data warehouses together, in a unified architecture where every team can work together on the same complete and authoritative source of data.

Again, we’re excited to be bringing this new data visualization and dashboarding experience to our customers. The integrated Redash experience is currently available in private preview, and you can sign up for the private preview waitlist to be the first to try it out.

Read the Redash team’s blog post on redash.io


以上所述就是小编给大家介绍的《Welcoming Redash to Databricks》,希望对大家有所帮助,如果大家有任何疑问请给我留言,小编会及时回复大家的。在此也非常感谢大家对 码农网 的支持!

查看所有标签

猜你喜欢:

本站部分资源来源于网络,本站转载出于传递更多信息之目的,版权归原作者或者来源机构所有,如转载稿涉及版权问题,请联系我们

高效程序的奥秘

高效程序的奥秘

沃瑞恩 / 冯速 / 机械工业出版社 / 2004-5 / 28.00元

本书适合程序库、编译器开发者及追求优美程序设计的人员阅读,适合用作计算机专业高年级学生及研究生的参考用书。  本书直观明了地讲述了计算机算术的更深层次的、更隐秘的技术,汇集了各种编辑的小技巧,包括常购的任务的小算法,2的幂边界和边界检测、位和字节的重排列、整数除法和常量除法、针对整数的基涵义,空间填充曲线、素数公式等。一起来看看 《高效程序的奥秘》 这本书的介绍吧!

HTML 编码/解码
HTML 编码/解码

HTML 编码/解码

SHA 加密
SHA 加密

SHA 加密工具

RGB CMYK 转换工具
RGB CMYK 转换工具

RGB CMYK 互转工具