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Databases, data science & more

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Publisher ‏ : ‎ O’Reilly Media; 1st edition (January 29, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 633 pages
ISBN-10 ‏ : ‎ 149196927X
ISBN-13 ‏ : ‎ 978-1491969274
Item Weight ‏ : ‎ 2.2 pounds
Dimensions ‏ : ‎ 7 x 1.3 x 9.1 inches

Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale

In today’s data-driven world, businesses are constantly looking for ways to harness the power of big data to drive insights and make informed decisions. One technology that has emerged as a key player in this space is Hadoop, an open-source framework that allows for the distributed processing of large data sets across clusters of computers.

However, implementing Hadoop at an enterprise scale requires careful planning and architecture to ensure smooth operations and efficient data processing. In this guide, we will discuss the key considerations and best practices for architecting modern data platforms using Hadoop at scale.

1. Scalability: One of the key advantages of Hadoop is its ability to scale horizontally, allowing organizations to add more nodes to their clusters as their data and processing needs grow. When architecting a Hadoop platform, it’s important to design for scalability from the start, ensuring that the system can easily expand to accommodate larger workloads.

2. High availability: Data is a critical asset for any organization, and downtime can have serious consequences. Ensuring high availability is essential when architecting a Hadoop platform, with redundant systems and failover mechanisms in place to minimize disruptions and ensure data integrity.

3. Security: With the increasing volume and diversity of data being processed by Hadoop platforms, security is a top concern for organizations. Implementing robust security measures, such as encryption, authentication, and access controls, is essential to protecting sensitive data and ensuring compliance with regulatory requirements.

4. Data governance: As data volumes grow, so does the complexity of managing and governing that data. Establishing clear policies and procedures for data governance, including data quality, metadata management, and lineage tracking, is crucial for maintaining the integrity and reliability of the data processed by Hadoop platforms.

5. Performance optimization: To maximize the performance of a Hadoop platform, it’s important to optimize the configuration of the system, including tuning parameters such as memory allocation, disk I/O, and network bandwidth. Monitoring and fine-tuning the system regularly will help identify bottlenecks and ensure optimal performance.

By following these best practices and considerations, organizations can architect modern data platforms using Hadoop at scale, enabling them to unlock the full potential of their data and drive business success in the digital age.
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