Understanding modern enterprise data lake architecture

by Dynamix Group Writer

Data lakes are a critical asset that every enterprise maintains  to expand the scope of machine learning, artificial intelligence, and metadata processing. Organizations that invest in modernizing their data lakes can uncover significant benefits such as faster processing, enhanced flexibility, elimination of silos, and unlimited query protocols.

Along with workload migration, modernizing the enterprise data lake is a critical step to becoming more agile within industries as well. With our rapidly evolving technological ecosystem, it’s important to understand the core technical requirements and strategic throughput necessary to launch and maintain a modern enterprise data lake.  

Challenges with traditional data lakes

Traditional data lake architectures lack the scale necessary to store large amounts of data coming from multiple sources. They also require process-heavy protocols, such as data cleansing, loading, structuring, etc. This may create latency in data insight derivation, as the traditional architecture sorts through the incoming information.

Additionally, enterprises would have to compromise in certain areas to maintain data fidelity for the data lake. This would necessitate the preservation of certain information and the elimination of others.

There may also be compliance-related issues with storing sensitive information within traditional data architecture. Enterprises may have to modernize their data lakes to remain compliant with industry regulations while ensuring they’re following the best practices in cybersecurity protocols.

The modern data lake architecture

The modern data lake focuses on enabling organizations to store, manage, and access data in a highly secure manner while driving business insights from the unstructured information present. The rise of the hybrid data lakes (on-premise + cloud) also allows enterprises to be more agile while analyzing raw data at a much faster pace via the Lambda architecture (batch + speed processing).

Modern data lakes are also allowing enterprises to store and process data from multiple sources, such as from IoT devices and connected systems. This is allowing them to move and analyze data at a much faster rate through analytical sandboxes and data cataloging. This also promotes faster time to deployment, as well as faster time to insight derivation across applications leveraging the data lake.

Benefits of modern data lake structures

Research from SAS suggests that 49% of technology professionals consider data exploration, through scale, as the primary benefit of adopting modern data lakes. CIOs, technical leads, and database managers can have access to rich insights when they adopt more flexible data lake architecture.

There is also a direct business advantage to adopting modern data lakes as well. The increased flexibility gives rise to quicker indexing of corporate documentation, lower defects in data quality, while simultaneously reducing the cost of running data systems.

Research from Aberdeen Group shows that enterprises that invested in a modern data lake outperformed their peers by 9% in organic revenue generation. That’s why organizations trust the leaders in enterprise data lake implementation, such as Impetus Technologies, to develop comprehensive data lakes for increased agility in information processing.  

Modern cloud-based data lakes also simplify the management of data across the enterprise. With a unified pool of unstructured information, the governance and access control of the data becomes highly streamlined. Additionally, enterprise researchers can save time by evaluating the raw information in the data lake to find preliminary value instead of performing new experiments within different data warehouses. 

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Created on Apr 14th 2020 04:19. Viewed 327 times.

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