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NYC Apache Spark Overview: May 22
Mon, May 22, 2017, 9:00 AM – 5:00 PM EDT
This one-day course is for data engineers, analysts, and architects; software engineers; IT operations; and technical managers interested in a brief hands-on overview of Apache Spark.
The course covers core APIs for using Spark, basic internals of the framework, SQL and other high-level data access tools, as well as Spark’s streaming capabilities and machine learning APIs. Each topic includes slide and lecture content along with hands-on use of a Spark cluster through a web-based notebook environment. *
Duration: 1 Day, Full Time (9AM to 5PM)
We will have a break from noon to 1pm; lunch will not be provided, but there are several options nearby.
• Basic Python, Scala, or SQL
After taking this class you will be able to:
• Experiment with use cases for Spark and Databricks, including extract-transform-load operations, data analytics, data visualization, batch analysis, machine learning, graph processing, and stream processing.
• Identify Spark and Databricks capabilities appropriate to your business needs.
• Communicate with team members and engineers using appropriate terminology.
• Build data pipelines and query large data sets using Spark SQL and DataFrames.
• Execute and modify extract-transform-load (ETL) jobs to process big data using the Spark API, DataFrames, and Resilient Distributed Datasets (RDD).
• Analyze Spark jobs using the administration UIs and logs inside Databricks.
• Find answers to common Spark and Databricks questions using the documentation and other resources.
*Note: Safari and Internet Explorer are not supported. You will need to bring your own laptop.
The creators of Apache Spark spun out of UC Berkeley to start Databricks in 2013. At Databricks we continue to grow the Spark project via software development, roadmap planning, and fostering the community. We have deeply integrated our Spark engineering efforts and our training program. The lead committers on Spark help design, create, and review our training curriculum and courseware. When you learn about Spark from Databricks you are learning from the Authority on Spark.