Key factors ensuring successful Hadoop data warehouse implementation

by Dynamix Group Writer

Hadoop is one of the most widely leveraged data warehouse architectures, enabling enterprises to scale-up in an agile and cost-effective manner. From large financial institutions to leading technology start-ups, Hadoop is used as a viable and secure data warehouse for faster big data processing. Firms need to adhere to the best practices when performing an ETL data migration to the Hadoop platform, especially when storing larger volumes of structured and non-structured enterprise data.  

Analyzing the benefits of Hadoop distribution

The traditional version of Hadoop is an open-source framework provided by the Apache Software Foundation and can be readily adapted by enterprises regardless of scale or scope of requirements. However, firms may want to explore Hadoop distributions, which are commercial versions of the Hadoop architecture designed to provide business-specific applications, better cluster support, and comprehensive management tools.

Hadoop distributions are also better suited for larger enterprises that want to scale-up their operations to the cloud. Firms can calculate their exact requirements in terms of data volume storage, expected growth, replication factor, compression rate, etc. to gain better insights about their cluster size. Hadoop distributions can also provide better workload distribution, as they provide more customized solutions in terms of cluster structuring, resource management, and data analytics.

Leveraging an agile and iterative approach

Enterprises may want to scale rapidly, based on the availability of Hadoop clusters and the capabilities of the architecture. However, it is ideal to initiate a Hadoop data warehouse program with business cases that can be iterated upon. By leveraging smaller Hadoop use cases, firms can better understand of how Hadoop can benefit their operations.

Additionally, enterprises need to have a customized approach to scaling-up so that they do not face challenges in data management, resource management, or data conversion. The Hadoop Distributed File System (HDFS) can be customized to drive key adjustments such as View File System (ViewFs), distributed scheduling, NameNode garbage collection tuning, etc. Enterprises can also limit the number of smaller files that filter through, as well as upgrade their versions frequently to remain agile in their implementation.

Institutions can work with the experts in the domain of Hadoop data warehouse migration, such as Impetus Technologies, to start experiencing scale-driven benefits in an agile manner.

Embedding robust cybersecurity measures

A critical reason behind a successful enterprise-level Hadoop implementation is the embedding of core cybersecurity measures. This involves the encryption of data channels feeding the warehouse, as well as the data storage protocols (data at rest encryption) being leveraged. Transparent data encryption (TDE) and column-level encryption should also be leveraged to protect the entire database present within the Hadoop data warehouse for comprehensive protection.

Additionally, incoming data from various sources should also be authorized to feed into the data warehouse only if they follow strict encryption and data transfer protocols. Tier-based access should also be implemented, with employees being made aware of common cybersecurity practices relating to social engineering, spoofing, phishing, etc. to establish holistic data protection.

 

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Created on Aug 25th 2020 02:31. Viewed 282 times.

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