Continution to previous post - How to Address Big Data Challenges (PART 1 of 6)
Managing the complexity of data integration and preparation
Big data platforms address the challenges of gathering and storing massive amounts of data of various types, as well as the rapid retrieval of data required for analytics. The data collection process, however, can be difficult.
Constant updating is required to maintain the integrity of an organization's acquired data repositories. This necessitates access to a wide range of data sources as well as specialized big data integration solutions.
Some businesses utilize a data lake as a catch-all repository for large amounts of big data gathered from many sources without considering how the different data would be combined. Various business domains, for example, generate data that is useful for joint analysis, but this data typically has ambiguous underlying semantics that must be resolved.
Ad hoc project integration is discouraged since it can result in a lot of rework. It's often advisable to adopt a strategic strategy to data integration for the best ROI on big data projects.
Effortlessly and economically scaling huge data systems
Businesses might waste a lot of money holding huge data if they don't plan how to use it. Big data analytics begins with data ingestion, which organizations must comprehend. Curating organizational data repositories also necessitates consistent retention strategies to cycle out old data, which is especially important since as data from before the COVID-19 epidemic is typically inaccurate in today's market.
As a result, before adopting big data systems, data management teams should map out the types, formats, and uses of data. However, it is easier said than done.
Frequently, you start with one data model and expand out, only to discover that the model no longer fits your new data points, resulting in technical debt that must be resolved.
A generic data lake with the right data structure can let you reuse data more efficiently and cheaply. Within a data lake, Parquet files, for example, frequently offer a superior performance-to-cost ratio than CSV dumps.
To be continued... How to Address Big Data Challenges (PART 3 of 6)
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