Automate the ingest of big data in a secure and fast manner
Challenge: Data Lake Issues
One of the key challenges companies face in moving data onto their Data Lake is the lack of data engineers available with the required skills and experience to create the scripts to correctly move the data.
Engineers with these skillsets are scare. Using engineers without the correct experience or skill-sets to do the work may result in the data being incorrectly ingested resulting in incorrect data at the destination.
In addition, manual coding of ingest scripts will lead to code management and deployment issues as the Data Lake scales and the data grows.
Impact: Ingest Data From Any Data Source
BDM Control enables you collect data from different data sources – EDW’s (Oracle, Teradata, DB2, etc.) – Files (AVRO, CSV, JSON, etc.) – Streams (Kafka, Spark Structured Streamin, etc.) – and in minutes move this data onto your Data Lake, where you can derive value from the data through Machine Learning and Analytics.
Solution: Real-time Data Ingestion Using Spark
The BDM Ingestion module allows data be moved from source to destination, with our automated solution eliminating coding and architectural errors.
Spark is used as the processing environment providing enhanced security as all activities on the data are carried out in memory, with no copies being saved to disk
Data is automatically normalized during the movement stage, where types and values are converted to enable the data to be ingested properly in the destination source.
Schema detection is carried out on each ingest, ensuring that all changes to the source schema are immediately replicated at destination
Ingestion connectors are written and available for most data sources
Opportunity: Speed Up Your Pipeline Creation
While hiring an experienced Data Engineers is costly, BDM removes the need for maintaining a large team of Data Engineers and decreases the workload on the existing team. With a successful deployment of the ingestion, multiple data pipelines can be created in minutes rather than days, increasing the speed of delivery and accuracy of your Hadoop solution to your internal clients.