How To Choose Architectural Patterns In Hadoop For Successful Deployments

Today, Hadoop has become one of the most successful data management platforms so far. Enterprises of all sizes love using Hadoop streamlining bid data so that it can be transformed in best possible way for maximum business profits. There are different Apache technologies o frameworks that can be used to complement data management services. Streamlining use cases is an art but in actual you have to decide on architectural patterns that can help you the most.

                      

Patterns for Hadoop architect

  • Stream ingestion : Involves event persistence using different Hadoop frameworks
  • NRT event processing : It includes event processing like flagging, altering, transforming and filtering etc. It does not process latencies.
  • NRT event partitioning : it works in the same way as NRT event processing but slightly better as compared to earlier. Here, processes are also partitioned for more relevant data management. The best part is that latencies can also be processed using this NRT pattern.
  • Complex topologies : it helps in getting real answers for more accurate and precise outputs. All of these patterns for Hadoop architect should be implemented in maintained, proven and tested way.

Let us check out all these patterns in detail for better understanding and improved implementation –

STREAM INGESTION - INVOLVES EVENT PERSISTENCE USING DIFFERENT HADOOP FRAMEWORKS.

Kafka and flume are two most important systems for streaming ingestion but the confusion is how they relate together for precise outcomes. You don’t have to get confused as their relationship can be simplified with better understanding of components used along. In simple words, flume is the system that takes input, process it and releases the output. At the same time, Kafka works as a pipe or interface between different flume components.

NRT EVENT PROCESSING – IT INCLUDES EVENT PROCESSING LIKE FLAGGING, ALTERING, TRANSFORMING AND FILTERING ETC. IT DOES NOT PROCESS LATENCIES.

It includes metadata management with the help of use cases or some eternal factors. Here, you have to decide either data should be transformed or other external action should be applied on data. Flume inceptor will accept the processes, actions will be taken on those processes, and it interacts with local storage memory for better data management. Decision are usually based on logic and Cloudera Navigator can also be integrated for unified user interface that can monitor, filter, or configure the services in better way.

NRT EVENT PARTITIONING – IT WORKS IN THE SAME WAY AS NRT EVENT PROCESSING BUT SLIGHTLY BETTER AS COMPARED TO EARLIER. HERE, PROCESSES ARE ALSO PARTITIONED FOR MORE RELEVANT DATA MANAGEMENT. THE BEST PART IS THAT LATENCIES CAN ALSO BE PROCESSED USING THIS NRT PATTERN.

If data is available in large volume then it is difficult to manage and process. Here you have to Hbase again and again to overcome this problem. To reduce overall headache, best solution is to divide processes so that incoming process can match up the output. It splits up your data in most logical way that can be integrated later for unified business decisions.

COMPLEX TOPOLOGIES – GET REAL ANSWERS FOR OBTAINING MORE ACCURATE AND PRECISE OUTPUTS. ALL OF THESE PATTERNS FOR HADOOP ARCHITECT SHOULD BE IMPLEMENTED IN MAINTAINED, PROVEN AND TESTED WAY.

  1. It can be used quickly than other tools can help.
  2. Batch processes and steaming processes is much easier then it was done earlier
  3. Powerful engine to accelerate data management program
  4. Data can be scaled or batched successfully.
  5. Data loss is almost negligible
  6. You can use SQL or ML libraries freely whenever you need them.

We have given detailed review of all patterns for Hadoop architect & developer. Now you have to decide on best according to use case and your business equirement.