Midhun S.

Midhun S.

Big Data / Cloud Data Engineer

Thiruvananthapuram , India

Experience: 10 Years

Midhun

Thiruvananthapuram , India

Big Data / Cloud Data Engineer

57600 USD / Year

  • Immediate: Available

10 Years

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About Me

Experienced Big data/Cloud  Engineer with a demonstrated history of working in the Big Data and Cloud technologies. Skilled in Hadoop,Spark,Kafka,Solr,Map Reduce,Hive,HBase and other related cloud technologies

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Portfolio Projects

Realtime Streaming Application

Company

Realtime Streaming Application

Description

Realtime streaming framework that makes the streaming process simpler and more productive. IRS offers guided UI for the end to end data pipeline creation, execution and monitoring. Possible to implement data pipe line using Spark, Kafka, Flume, Twitter, REST and others.

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RSS Feed Search Engine

Company

RSS Feed Search Engine

Description

RSS Feed Search Engine is a search engine developed for a client in Azure Cloud, that capable to search the RSS feed keywords for the business requirements. Management need to identify the users feed backs, news and review comments.

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Logistics Dashboard

Company

Logistics Dashboard

Description

The logistics dashboard application is designed to ingest and process data files to big data environment. The input files that are ingested are transformed to use full format, before being inserted in various HIVE tables. Those tables are eventually querying to build useful reports for the marketing team.

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Data Migration Applications

Company

Data Migration Applications

Description

Data Migration applications are fetching the data from the various data environment to Hadoop stack. Sqoop, Flume, Solr and other migration tools are used based on the client requirements. The data fetch jobs trigger using commands, shell scripts, Oozie workflows.

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Verifications

  • Profile Verified

  • Phone Verified

Preferred Language

  • English - Fluent

Available Timezones

  • Dubai [UTC +4]

  • New Delhi [UTC +5]

  • China (West) [UTC +6]

  • Singapore [UTC +7]

  • Hong Kong (East China) [UTC +8]