Apache Spark vs Trifacta: What are the differences?
What is Apache Spark? Fast and general engine for large-scale data processing. Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
What is Trifacta? Develops data wrangling software for data exploration and self-service data preparation for analysis. It is an Intelligent Platform that Interoperates with Your Data Investments. It sits between the data storage and processing environments and the visualization, statistical or machine learning tools used downstream.
Apache Spark and Trifacta can be categorized as "Big Data" tools.
Some of the features offered by Apache Spark are:
- Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk
- Write applications quickly in Java, Scala or Python
- Combine SQL, streaming, and complex analytics
On the other hand, Trifacta provides the following key features:
- Interactive Exploration
- Automated visual representations of data based upon its content in the most compelling visual profile
- Predictive Transformation
Apache Spark is an open source tool with 23.1K GitHub stars and 19.9K GitHub forks. Here's a link to Apache Spark's open source repository on GitHub.
Sign up to add or upvote prosMake informed product decisions
Sign up to add or upvote consMake informed product decisions
What is Apache Spark?
What is Trifacta?
Need advice about which tool to choose?Ask the StackShare community!
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions