Data engineers play the most technical role in getting transformed data available for business decisions. Their role involves building and maintaining a pipeline to handle data transformation processes and this is a herculean task which constantly pops up challenges such as:
- Too much data to handle - the size of data to be transformed is most times not at par with the infrastructure in place hence, constant pipeline crash.
- Time constraint - Building a robust infrastructure to handle data transformation effectively is time intensive.
- Data integration problems - most times, it's difficult to ingest data seamlessly from various sources considering where they are stored and the formats in which they are stored.
- Pipeline maintenance - Data engineers don't just have to work round the clock for the pipeline to be built, they also have to ensure that it's working properly.
- Zero flexibility - Because data engineers solely have to focus on building a pipeline that works, they have less time to customize pipelines that suit the business needs per time.
- Long hours of coding to clean up data and build a pipeline.
- Ingest high influx of data without fear of a pipeline crash with our superb auto scaling capability.
- Carry out data engineering processes like integrating and transforming data in less time.
- Seamlessly integrate data from multiple sources for transformation.
- Cancel the worry on pipeline maintenance.
- Build custom data pipelines without sweating it out
- Automate data pipeline executions
- Enhance data quality with accurate reports and jobs scheduling
- No-code most times and code where necessary