You want to make use of the massive amounts of data that are coming from a wide variety of data sources. Here are the key challenges you’ll face in your data lake architecture:
Query performance is often limited with concurrency and scalability issues. Also, a complex architecture can result in broken data pipelines, performance degradation (such as slow transformation), and error-prone data movement outside of your data lake. These errors, plus a rigid architecture can result in governance and security risks.
The last, but not least, challenge is data ingestion. Ingesting and integrating data from hundreds or even thousands of diverse sources can be overwhelming. When moving information to a data lake, many sources require custom coding and individual agents. This can quickly drain your IT resources. Moving data to a data lake can also have a negative impact on source systems, causing disruption and loss of data availability. Big data analytics tools that have universal and real-time data ingestion capabilities can speed and simplify ingestion of data from a wide variety of data sources, including live data streams.