Data Processing

In data processing, we basically process the data which was ingested. It could involve any of the below:

• Data cleaning

• Null handling

• Data integration from various data source to a single data source

• Applying custom business rules

• Transformations, etc.

There are various tools which could be used for data processing. Open Source ETL tools like Talend, Pentaho data integrator (PDI) could be used for data processing. Codes could be written using Python or Java as well. Besides these, there are tools like Apache Spark, Flume, Flink, sqoop, apache storm, etc., which could be used for data processing.

Data processing tools can be categorized broadly into two types:

1. In-Memory: Tools like Apache Spark comes under this category. It takes the entire data into a local or distributed RAM memory, and then on top of that processing is done, thus performance is extremely fast. Since the entire data gets loaded into the In-Memory, the hardware requirement is generally on the higher side. These In-Memory tools can further be categorized into centralized and distributed.

2. Filesystem or DB-Driven: In this type of tool, data is stored in the DB itself and only that data is fetched which is required for processing. Thus, often, the performance of these tools is lower as compared to In-Memory tool though the hardware requirement is not that high.

We have ample experience with using most of these data processing tools, primarily open source. We can help you achieve your business objectives at a fraction of cost compared with other proprietary processing engines, with same or even better quality.