Apache Spark Streaming vs. Rockset

Apache Spark Streaming is a tool that we are using for almost a year and is excellent in managing batch processing. It is user-friendly. Using it, we can even process our massive data in fractions of seconds. Its pricing is its other plus point. Only its In-memory processing is its demerit as it occupies a large memory.

Incentivized

1. Usage based billing - helpful in creating query lambdas and workflow around them. 2. Fast data refresh and scheduled running of queries is required. 3. Wherever real-time data is in play in terms of visibility.

Incentivized

• It is amazing in solving complicated transformative logic.
• It is straightforward to program.
• It is a very quick tool.
• It processes large data within a fraction of seconds.

Incentivized

• Fast ingest and real-time indexing
• Complex SQL queries run really fast
• Lower cost in terms of deployment.

Incentivized

• There must be more documentation.
• It is a profoundly complex tool.
• Its in-memory processing consumes massive memory.

Incentivized

• User interface
• AI in queries, auto-generated queries
• Graphing tools

Incentivized

Apache Spark Streaming stands above all the huge data transformative tools because of its speed of processing which was quite slow in Presto as it takes a lot of our time in the data processing. Spark, comfortably provides integration with Jupyter like notebook environment. and Spark's combination with Jupyter and Python results in enhancing the speed .

Incentivized

• Cost and time-effective tool for our business.
• We can integrate with Jupyter with many conveniences.
• Its high-speed data processing has proved beneficial for us.

Incentivized

• Didn't need to invest in an external billing software.
• Major customer feature ask was fulfilled - realtime usage overview

Incentivized