Engineering your spark application for testing
Advice on how to design and build your Apache Spark application for testability
Map Reduce Jobs
Map Reduce is a programming model created to process big data sets. It's oftentimes utilized in the act of distributed computing for different devices. Map Reduce jobs involve the splitting of the input data-set into different chunks. These independent sectors are then processed in a parallel manner by map tasks. The framework will then sort the map outputs, and the results will be included in "reduce tasks." Usually, the input and output of Map Reduce Jobs are kept in a file-system. The framework is then left in charge of scheduling, monitoring, and re-executing tasks.
Map Reduce can be used in jobs such as pattern-based searching, web access log stats, document clustering, web link-graph reversal, inverted index construction, term-vector per host, statistical machine translation and machine learning. Text indexing, search, and tokenization can also be accomplished with the Map Reduce program.
Map Reduce can also be used in different environments such as desktop grids, dynamic cloud environments, volunteer computing environments and mobile environments. Those who want to apply for Map Reduce jobs can educate themselves with the many tutorials available in the internet. Focus should be put on studying the input reader, map function, partition function, comparison function, reduce function and output writer components of the program. Najem Map Reduce Developers
Map Reduce can be used in jobs such as pattern-based searching, web access log stats, document clustering, web link-graph reversal, inverted index construction, term-vector per host, statistical machine translation and machine learning. Text indexing, search, and tokenization can also be accomplished with the Map Reduce program.
Map Reduce can also be used in different environments such as desktop grids, dynamic cloud environments, volunteer computing environments and mobile environments. Those who want to apply for Map Reduce jobs can educate themselves with the many tutorials available in the internet. Focus should be put on studying the input reader, map function, partition function, comparison function, reduce function and output writer components of the program. Najem Map Reduce Developers
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Problem Statement: Design scalable pipeline using spark to read customer review from s3 bucket and store it into HDFS. Schedule your pipeline to run iteratively after each hour. Create a folder in the s3 bucket where customer reviews in json format can be uploaded. The Scheduled big data pipeline will be triggered manually or automatically to read data from The S3 bucket and dump it into HDFS. Use Spark Machine learning to perform sentiment analysis using customer review stores in HDFS. Data: You can use any customer review data from online sources such as UCI
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Design, develop and maintain BI Solution using Redshift, Tableau & Alteryx Optimize Redshift Data warehouse by implementing workload management, sort keys & distribution keys Required Skills: 4+ years of experience using databases such as Oracle, MS SQL Server 1+ Must have hands on experience of Amazon Redshift Architecture. 1+ AWS Pipeline knowledge to develop ETL preferably using AWS Glue for data movement to Redshift Strong knowledge on AWS environment and Service knowledge with S3 storage understanding Strong knowledge multiple cloud technologies including VPC, EC2, S3, Amazon API Gateway, DynamoDB, SimpleDB, AWS Route 53 Should understand nuances of moving data from RDBMS sources to Columnar DB (Redshift) Hands on experience in Data Warehouse (DW) and BI tool...
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