Hadoop architecture matters not just set of tools

Hadoop architecture matters not just set of tools
Apache hadoop architecture

Planning a big data ecosystem for your business requires critical thinking at finding possibilities while considering complexity at each stage. Apache hadoop is enabling enterprises manage their growing data while misconceptions prevail as hadoop is dying fast!

Ultimate goal of any big data project depends on its ability to manage the following

Store – System’s ability to capture and store data with high velocity and volume.
Process – System’s ability to provide computational power to the stored data.
Access – System’s ability to integrate processed data and make it available to the user.

While seemingly simple definition has deep complexity inside.

Why hadoop?

Hadoop is an open-source data platform that runs on commodity hardware of multiple servers to form a cluster that can be scaled out easily and provides a fault tolerant system. It is an ideal for distributed data processing and storage.

Knowing hadoop is not enough?

While Hadoop is becoming de facto standard for most bigdata projects it requires strong analytical skills. Hadoop can manage all kinds of data – structured, unstructured, audio, video, pictures or emails. However, you must be able to identify right source of data and think of architectural aspects. Whether the application requires historical data or live streaming data? Do you need to combine data from multiple sources? Do you need to perform cleansing, transformation or classification of the data? How your users want to query the data? Thoughtful architecture of hadoop ecosystem that can foresee possibilities can add value to your business while handling complexity. Thus it creates win-win situation for your customers as well as business stakeholders.

Hadoop allows you to process your data using it’s native distributed computation power-MapReduce. You may choose from variety of frameworks and tools such as Hive, Pig, Oozie, Tez, Spark, Storm, Elasticsearch, Kibana and so on to enhance capabilities of your ‘big data project’. Right selection of tools will complement your ‘big data project’ with required computational power and analytic capability without any overheads.

Hadoop adoption is getting bigger and bigger across industries as hadoop solutions can make your data fluid and let it flow out of silos irrespective of its native format so you can put it in the right hand.

Krishna Meet is a technical writer and UX designer having core interest in analytical dashboard designing. Majority of her career span was into tech-writing. However, she thrives by intersecting multiple skill-sets : DITA documentation, script writing for tech-videos, business analysis, and UX design. She is a voracious reader and possesses Masters degree in Computer Science. Her interest in agile methodologies and user-centered design has landed her a techno-functional role at Brevitaz.

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