A completely open web-based notebook that enables interactive data analytics
Apache Zeppelin is a new and incubating multi-purposed web-based notebook which brings data ingestion, data exploration, visualization, sharing and collaboration features to Hadoop and Spark.
Interactive browser-based notebooks enable data engineers, data analysts and data scientists to be more productive by developing, organizing, executing, and sharing data code and visualizing results without referring to the command line or needing the cluster details. Notebooks allow these users not only allow to execute but to interactively work with long workflows. There are a number of notebooks available with Spark. iPython remains a mature choice and great example of a data science notebook. The Hortonworks Gallery provides an Ambari stack definition to help our customers quickly set up iPython on their Hadoop clusters.
Apache Zeppelin is a new and upcoming web-based notebook which brings data exploration, visualization, sharing and collaboration features to Spark. It support Python, but also a growing list of programming languages such as Scala, Hive, SparkSQL, shell and markdown.
The various languages are supported via Zeppelin language interpreters.
Data discovery, exploration, reporting and visualization are key components of the data science workflow. Zeppelin provides a “Modern Data Science Studio” that supports Spark and Hive out of the box. Actually, Zeppelin supports multiple language backends which has support for a growing ecosystem of data sources. Zeppelin’s notebooks provides interactive snippet-at-time experience to data scientist. You can see a collection of Zeppelin notebooks in the Hortonworks Gallery.
Also when you are done with your notebook and found some insight you want to share, you can easily create a report out of it and either print it or send it out.
At Hortonworks we believe that Spark & Hadoop are Perfect Together. And that Zeppelin is a key component to accelerate data science solutions.
Even with notebooks the data wrangling process remains challenging. Often data scientists struggle with feature engineering, algorithm selection, tuning, sharing their work with others and deploying their work into production.
We are working to improve the Zeppelin notebook in the community. We have added Hive Interpreter to Zeppelin,
and are working to improve the editor to make it more stable. We are deepening our involvement in the Zeppelin community to help deliver features such as security, summary statistics, context sensitive help to improve data development experience.
Access to Latest Innovation
Notebooks help data scientists be productive fast and deal with data and visualization without having to worry about Spark command lines and cluster details. There are many notebooks choices available with Spark. iPython remains a mature choice and we have an Ambari stack definition available to help our customers quickly set them up on their Hadoop clusters.
Apache Zeppelin is new and upcoming notebook which brings data exploration, visualization, sharing and collaboration features to Spark. We are excited about this project and are working with the community to bring Zeppelin to maturity. We plan to make the Zeppelin ready for production use by adding security, stability, R support and make the visualization more intuitive.
We are working to improve the Zeppelin notebook in the community. We added Hive Interpreter to Zeppelin, are working to improve the editor and make it more stable and some of the JIRAs tracking the work are ZEPPELIN-11, ZEPPELIN-16, ZEPPELIN-19, ZEPPELIN-33, ZEPPELIN-188, ZEPPELIN-223.
We are deepening our involvement in the Zeppelin community to help deliver features such as security, summary statistics, context sensitive help to improve data science experience.
Introduction Hadoop has always been associated with BigData, yet the perception is it’s only suitable for high latency, high throughput queries. With the contribution of the community, you can use Hadoop interactively for data exploration and visualization. In this tutorial you’ll learn how to analyze large datasets using Apache Hive LLAP on Amazon Web Services […]
A very common request from many customers is to be able to index text in image files; for example, text in scanned PNG files. In this tutorial we are going to walkthrough how to do this with SOLR. Prerequisites Download the Hortonworks Sandbox Complete the Learning the Ropes of the HDP Sandbox tutorial. Step-by-step guide […]
Introduction In this tutorial, you will learn about the different features available in the HDF sandbox. HDF stands for Hortonworks DataFlow. HDF was built to make processing data-in-motion an easier task while also directing the data from source to the destination. You will learn about quick links to access these tools that way when you […]
Introduction JReport is a embedded BI reporting tool can easily extract and visualize data from the Hortonworks Data Platform 2.3 using the Apache Hive JDBC driver. You can then create reports, dashboards, and data analysis, which can be embedded into your own applications. In this tutorial we are going to walkthrough the folllowing steps to […]
Apache Zeppelin on HDP 2.4.2 Author: Vinay Shukla In March 2016 we delivered the second technical preview of Apache Zeppelin, on HDP 2.4. Meanwhile we and the Zeppelin community have continued to add new features to Zeppelin. These features are now available in the final technical preview of Apache Zeppelin. This technical preview works with […]
The Hortonworks Sandbox is delivered as a Dockerized container with the most common ports already opened and forwarded for you. If you would like to open even more ports, check out this tutorial.
Introduction R is a popular tool for statistics and data analysis. It has rich visualization capabilities and a large collection of libraries that have been developed and maintained by the R developer community. One drawback to R is that it’s designed to run on in-memory data, which makes it unsuitable for large datasets. Spark is […]
Apache, Hadoop, Falcon, Atlas, Tez, Sqoop, Flume, Kafka, Pig, Hive, HBase, Accumulo, Storm, Solr, Spark, Ranger, Knox, Ambari, ZooKeeper, Oozie, Phoenix, NiFi, HAWQ, Zeppelin, Atlas, Slider, Mahout, MapReduce, HDFS, YARN, Metron and the Hadoop elephant and Apache project logos are either registered trademarks or trademarks of the Apache Software Foundation in the United States or other countries.