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Bigdata with scala and java developer
marni manikanta
,
East Godavari, India
Experience
Other titles
Skills
I'm offering
I am a big data developer
Having good experience in java, scala, spark, Kafka, nifi and sql.
Having good experience in java, scala, spark, Kafka, nifi and sql.
Markets
United Kingdom
Language
English
Good
Ready for
Available
My experience
2018 - ?
job
senior software engineer
HCL Pvt. Ltd.
Software
2017 - 2018
job
Physician and patient views, Call center chat and CDR data
Merck & Co. Inc.
USA
Environment Hadoop(HDP 2.3), Spark, Hive, HBASE, Sqoop, Kafka, NIFI, Oozie.
Duration Jan 2017 to Aug 2018.
Patient Pathway analytics project provides a converged data lake that helps the enterprise to acquire, cure, store, process and visualize the end to end touchpoints of the pathway of each and every patients using the data ingested from heterogeneous data sources such as enterprise data warehouses, clinical databases, claims & cost data, healthcare marketers info, pharma, R&D data, patient behavior & sentiment data sets, Physician and patient views, Call center chat and CDR data, direct IOT log data of the end patients, click stream data of the patients for log analysis and marketing campaigns.
Using Batch analytics process, this system creates a 360-degree view of patient journeys around complex event processing (CEP), which performs event-by-event processing and aggregation with the data provided across all channels to identify the pain points and interaction of each and every patients to provide on time treatments, better patient experience, perform prescriptive analytics to create personalized low cost treatment planning, perform assisted diagnostics, and monitors patients vital signs of illness. Structured and semi structured Data from different source systems are injected using sqoop, flume loaded into HDFS are parsed and filtered using customized pig latin scripts enriched with UDFs, will be overwritten into HIVE managed tables as a staging and apply complex HQLs to join, aggregate, filter, convert and load the final data into final external flat and serde tables. Master patients/physician data that requires low latency and update data access will be loaded into HBASE table using hive-hbase handlers. Data in Hbase will be queried using Phoenix by UI team. Outbound feeds will be exported from hive and sent as feeds using scp or sqoop exported directly to the RDBMS systems.
Using Realtime streaming data generated from the health care devices connected to the in-patient and the out patients are monitored regularly to perform embedding analytics for performing actionable data insights using the predictive and proactive models designed by our data analytics team. Perform continuous queries on the near real-time time-sensitive data, generate heat maps, trigger notifications, sends alerts to the concerned team to monitor patient's vital signs of illness. Structured and semi structured Data from different devices are injected using NIFI place into Kafka message queue will be consumed using NIFI and stored into HBASE queried using Phoenix. Spark streaming and core processing implementation in progress.
Project #3 TRAVEL MANAGEMENT SYSTEM(TMS)
Environment Hadoop(HDP 2.3), Spark, Hive, HBASE, Sqoop, Kafka, NIFI, Oozie.
Duration Jan 2017 to Aug 2018.
Patient Pathway analytics project provides a converged data lake that helps the enterprise to acquire, cure, store, process and visualize the end to end touchpoints of the pathway of each and every patients using the data ingested from heterogeneous data sources such as enterprise data warehouses, clinical databases, claims & cost data, healthcare marketers info, pharma, R&D data, patient behavior & sentiment data sets, Physician and patient views, Call center chat and CDR data, direct IOT log data of the end patients, click stream data of the patients for log analysis and marketing campaigns.
Using Batch analytics process, this system creates a 360-degree view of patient journeys around complex event processing (CEP), which performs event-by-event processing and aggregation with the data provided across all channels to identify the pain points and interaction of each and every patients to provide on time treatments, better patient experience, perform prescriptive analytics to create personalized low cost treatment planning, perform assisted diagnostics, and monitors patients vital signs of illness. Structured and semi structured Data from different source systems are injected using sqoop, flume loaded into HDFS are parsed and filtered using customized pig latin scripts enriched with UDFs, will be overwritten into HIVE managed tables as a staging and apply complex HQLs to join, aggregate, filter, convert and load the final data into final external flat and serde tables. Master patients/physician data that requires low latency and update data access will be loaded into HBASE table using hive-hbase handlers. Data in Hbase will be queried using Phoenix by UI team. Outbound feeds will be exported from hive and sent as feeds using scp or sqoop exported directly to the RDBMS systems.
Using Realtime streaming data generated from the health care devices connected to the in-patient and the out patients are monitored regularly to perform embedding analytics for performing actionable data insights using the predictive and proactive models designed by our data analytics team. Perform continuous queries on the near real-time time-sensitive data, generate heat maps, trigger notifications, sends alerts to the concerned team to monitor patient's vital signs of illness. Structured and semi structured Data from different devices are injected using NIFI place into Kafka message queue will be consumed using NIFI and stored into HBASE queried using Phoenix. Spark streaming and core processing implementation in progress.
Project #3 TRAVEL MANAGEMENT SYSTEM(TMS)
Implementation, Processing, USA, Performing, Enterprise, Spark Streaming, Streaming, Health, Call Center, Hive, Pharma, Marketing, Spark, Kafka, Iot, Analytics, Management, Hadoop, Event, R, UI
My education
?
-
2015
Anna University
Bachelors, Computer Science
Bachelors, Computer Science
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