“Experts often possess more data than judgment.” – Colin Powell.
No company in today’s world can deny the power locked up in its data. Obvious is the fact that every company is engrossed in unleashing the mysteries their data has to offer with regards to their customers, products and implement robust strategies to drive the business. The problem however is the time taken to harness this data coupled with the ever increasing cost of this journey.
Few years back building Data Warehouses was thought of to be the ultimate solution which would help companies drive strategic business decisions, get customer insights, build prediction models and drive all major business KPI’s. Many were able to build successful DW’s that formed the cornerstone of their decision making. But with an evolving world the data formats and volumes kept changing and increasing thus making it difficult for the DW world to keep up the pace. The time had come to move out of the box of the structured world and embrace the world of BIG DATA. Thus started the evolution of “Data Lakes”.
A Data Lake defies the very basic definition of a Data Warehouse. Data Lake is a data repository that stores raw data in its native format which includes structured, semi structured as well as unstructured data.
The prime idea behind creation of a data lake is to have all the data sets generated by the business in one central repository. Data Lake creation does not pose a question on data consumption. Rather it provides a unique opportunity to business users to consume the same information in multiple ways without needing to have it sourced from the application system every time. Since a data lake is build atop BIG data platforms like Hadoop, the cost of storage automatically dives down. The Hadoop ecosystem further provides a gamut of tools and utilities which can be easily deployed to create meaningful data interpretations from even the unstructured formats. A team of data scientist can now study the business data as a single unit without having to be dependent on the silos.
Data Lake Creation Issues
Though organizations are moving towards creation of Data Lakes they face a lot of challenges, a few listed below
- What Data should be moved to Data Lake
- What tools to use for the Data movement
- Should data be cleansed and structured
- Which is the better Data Lake Platform
- How to take care of the ever changing data on a regular basis
- How to schedule the jobs
- How to add additional sources
- How to maintain a flow and consistency with the Data Warehouse
- How to get BIG Data experts to get the task done
- How to Audit the data flow
SPAR Ingestion Framework
Spar offers a unique solution that takes away all your worries of Data Ingestion. We offer an Ingestion Framework which is more of plug and play, offering you platform Independence and giving you the flexibility of doing real time data ingestion. It can also couple with the existing data warehouse and can create a three way data movement between the sources, warehouse and data lake without data loss thus promising data consistency.
- Supports Real Time, Near Real or Bulk Data Ingestion
- Augments various data transport capabilities through a pipeline by leveraging Apache Kafka
- Ability to ingest and transform data in all the popular formats – viz XML, JSON, TSV unstructured or RDBMS tables
- Output stream in a single common Avro format
- Ready architecture for Platform Migration and Integration
- Inbuildsupport for Change Data Capture
- Ensures seamless data movement between the sources and the data lakes
- Couples with the DW as well thus making migration from DW to DL an easy task
- Output can be pushed to multiple targets thus allowing greater flexibility for data consumption
- Inbuild audit and reporting mechanism
- Can be mounted over platform of choice