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Modern Batch Processing Advantages and Tools

Acknowledging Data Management
          Best Practices with DataOps


What is Modern Batch Processing?

Modern Batch Processing is a way to process the job in groups (known as batches), it is still alive. Modern Batch Processing technique is present since punch cards were used. Not only jobs but data also processed in the form of batches. Data not generated in the form of batches. It is generated in the form of a stream. Nowadays, a lot of data storage in the form of a stream, through many techniques data streamed from end to end (from the point were generated to the point stored). But when it comes to the task of Data Processing, then it’s processed in batches. This extraction, enrichment, transport, analysis and loading of data in the form of batches comes under Batch Processing powered by modern techniques.

How Processing Works?

Data Storage - It starts by analyzing the humongous amount of Data and "How to handle this amount of data." Batch Processing - Batch divided into the batches then filtered, stored into a Distributed Environment (for example HDFS). Data Analytics by storage - In Batch processing engines, batches undergo processing (for example Map-Reduce of HDFS). The size of the batch chosen by the system. Analysis and Reporting - To give insights into the data by using analysis and reporting of the data. The arrangement of Data - To migrate or to copy the data into the data storage, processing of the batches, analytics on stored data and managing the reporting layer.

Advantages of Modern Batch Processing

Monitoring - The data which comes for storage end and used for the other processes watched by monitor. The monitor observes the following things -
  • Errors
  • Files and directories
  • System availability
  • Processes
  • Overruns, under runs, and late starts
Dependency Management - It allows Dependency Management because in Batch Processing it is easy to monitor dependencies. Notifications Management - A batch scheduling/processing model gives the following notifications -
  • Data Job Failure
  • Data Server down
  • Data Service down
  • Data Events

Why Batch Processing Matters?

These are some points to show the importance of Batch Processing techniques - The techniques which support manual process (other than batch processing) fail to give any assurance regards of giving order timely. However, Batch processing has the power to do the same. Modern Batch processing also overcast manual process in giving any verification of the completeness of the previous operations. The changes in the files also handled by the Batch processing very efficiently which makes easy to analyze the changes in old files with the arrival of the new files. Time of the processing shifted to the hours when Batch Processing used. By using Modern Batch Processing Techniques, the computer can be saved while providing an overall high utilization rate which provides cost efficiency also. Modern Batch Processing uses many programs for different transactions. Modern Batch processing uses one system for many operations.

How to Adopt Processing?

For using this, the data should be divided into a distributed environment in the form of batches again from batches are mapped into an environment — the size of the batches chosen by the system, here data processed. The processes include Data Transformation, Data Migration, Copying data, and Data analytics. This whole mapping procedure handled in the Hadoop ecosystem by MapReduce Functionality. It also gives an edge that computation pursued in a distributed manner. While adopting Batch processing as a business model, consider the following activities and sub-tasks -
Activities Sub-Tasks
Process Model
  • Management of all activities involved.
  • Management of processing of the Models.
Creation of the Batch
  • Intentionally
  • Classification and categorization.
  • Scheduling of the Instance.
  • Analyzing Resource Capacity.
  • Assignment of the batches.
Execution of the Batch
  • Handling the mechanism of the Activation
  • Scheduling of the Batches
  • Making the strategy of the Execution
  • Acclimatization
  • Handling Vulnerability
The two usable types of Batch processing are - User-involved batch activities - This type of processing includes more user-oriented activities implemented using supplementary batching (if required). Automated batch activities - This type of processing requires machines with higher capacity, Artificial Intelligence Techniques, and Information Technology to an extent.

Best Practices of Modern Batch Processing

Speed gives an advantage in Mobility - The data processes pushed below as close to the system for achieving efficiency. The target for Efficiency in accessing the Data - With a lot of advantages of Batch Processing, there come some disadvantages too, one of them is that a failure in the batch performance scales down the whole system. Hence, to avoid it access the data efficiently. Place data near the Application Layer - To improve the performance the application layer placed near the Data layer. You would also love to explore how to build big data pipeline on Azure.

Modern Batch Processing Tools

Steps Tools
Storing of Data Azure Data lake store, Azure Storage Blob Containers
Processing of Batches Spark, Pig, Hive, Python, and U-SQL
Data Storing with Analytics Hive, Hbase, SQL Data Warehouse, MongoDB, DynamoDB, Spark SQL
Reporting and Analytics Python, Power BI, Azure Analysis Service
Arrangement of Data Oozie, Sqoop and Azure Data Factory

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