Last week, we held a live webinar — Apache Spark – The Unified Engine for All Workloads — to explain the real-world benefits to practitioners and enterprises when they build a technology stack based on a unified approach with Apache Spark.
This webinar features Ovum analyst Tony Baer, who will explain the real-world benefits to practitioners and enterprises when they build a technology stack based on a unified approach with Apache Spark.
This webinar will cover:
- Findings around the growth of Spark and diverse applications using machine learning and streaming.
- The advantages of using Spark to unify all workloads, rather than stitching together many specialized engines like Presto, Storm, MapReduce, Pig, and others.
- Use case examples that illustrate the flexibility of Spark in supporting various workloads.
The webinar is now accessible on-demand, and the slides used in the webinar are also downloadable as attachments to the webinar.
If you’d like free access to Databricks, you can access the free trial here.
Common webinar questions and answers
Click on the question to see answer
- Are there any projects under way that integrate Databrick/Spark with IBM BigIntegrate?
- What is the best practice for storing metadata for a Spark environment?
- For ETL batch loads which process high data volumes, how would memory size constraints for Spark be addressed?
- Is it advisable to use Spark on the data ingestion side as a replacement to Sqoop?
- Are there any plans to create a content based filtering recommendation algorithm in MLlib?
- When we create an application with Spark, we must control our cluster. So what’s the difference between Ambari and Yarn?
- Is Apache Spark (using Python or SparkSQL) replacing dimensional modeling process such as a star or snowflake schema?
- What is the release date for Spark 2.2.0?
- Are there any tools/utilities to convert MapReduce to Spark?
- What are the current differences between Spark available on Amazon AWS and the Databricks-provided version of Spark?
- What is the best open source front-end for Spark queries?