3 Important Steps to Simplify the Hadoop Management

Yeah it truly makes sense to be excited about the vast opportunities Apache Hadoop YARN based apps like Storm, Spark, Presto and others to offer considerable value to the business.


But, have you ever realized how it’s challenging to manage and maintain the Hadoop environment? There are people who without giving a thought to best practices fir big data system performance and stability, they lose faith in Hadoop. They do not take it as a difference maker, but a solution that gulped down their money.


Well, with the objective of accelerating big data adoption, the Hadoop environment must function to its maximum capability to meet end-user expectations.


Let’s talk about an example, think Big, which is a Teradata company, employs Hadoop platforms for various customers worldwide and has suggested three best practices that can improve your familiarity with Hadoop and enhance operations.


Power Workload Management Abilities: You know, workload management is important when you work in Hadoop environment. It’s simply because, big data systems are used for production in a larger way.


Even though you can deploy Hadoop cluster with guidelines provided by your distribution provider and it should be configured for your particular workloads. Administrators can make use of the workload management capabilities of YARN to determine which users access what system resources and when to cater to the service levels.


When you identify the workload management settings properly and adjust it, administrators can schedule the jobs to utilize the cluster resources to the maximum. By this way not only you can keep the Hadoop cluster’s footprint to a suitable size, but also increase the flexibility to match the resources as per the changing need of your business.


Do your best for Business Continuity: As your valuable data is stored and stages in Hadoop, it’s important to protect data and make the system available continuously. But, the replication capabilities of Hadoop aren’t enough to safeguard vital datasets from any unexpected incident. A standard three-way replication is enough to ensure safety of various data objects from corruption or loss. But it’s not an enough backup and disaster recovery plan.


The replication of Hadoop is designed to allow better fault tolerance and data locality for processing, however having three copies of the data in the same rack isn’t gonna protect it from unavoidable problems that will pop up. That is why there should be a backup of data to another data center via an enterprise data archive tool or cloud instance. This will protect the data from natural calamities, cyber-attacks or other unfortunate incidents.


In the course of business continuity, don’t look over NameNode backup. It stores a directory tree of files in HDFS (Hadoop Distributed File System) and records at the location where data is kept in the cluster. In case of a single point of failure, rebuilding the NameNode from scratch is really a time consuming effort that may cause considerable data loss. Hence, as you protection system goes up; it becomes even more important to backup NameNode with business data as well.


Those applications which are critical and depending upon Hadoop resources also need a high-level strategy. This calls for a plan that can be easily performed to make sure production workloads are not being messed up by unexpected circumstances.


So make sure to involve a process to reconstruct data sets from raw sources and/or voluntarily restorable offline backups of irreplaceable data sets.


Utilise your Hadoop Experience: While thorough documentation on Hadoop architecture, every day morning tasks and issue resolutions are necessary, there is no any replacement for experience. You just can’t sit that there won’t arrive an issue, definitely it will even after the application support processes have been documented. This is where experience comes handy. A particular skill set is required to administer and build on big data open source platforms, far beyond the regular DBA is trained to work.


Besides Hadoop admin experience, the big data application support teams of yours should have a sound technical background that helps for adapting to non-standard challenges. A senior technical person well-versed in resolving specifically hard challenges should also be there in the team. They will have a detailed hands-on-experience on custom application development in Hadoop, sound Linux familiarity to resolve complex issues.

And, what you need to get that experience, a suitable big data Hadoop training program!

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