yashwanth

• Working on Azure Data Factory to integrate data of both on-prem (MY SQL, Cassandra) and cloud (Blob storage, Azure SQL DB) and applied transformations to load back to Azure Synapse.
• Managed, Configured and scheduled resources across the cluster using Azure Kubernetes Service.
• Monitoring Spark cluster using Log Analytics and Ambari Web UI. Transitioned log storage from Cassandra to Azure SQL Dataware house and improved the query performance.
• Involved in developing data ingestion pipelines on Azure HDInsight Spark cluster using Azure Data Factory and Spark SQL. Also Worked with Cosmos DB (SQL API and Mongo API).
• Extensive Shell/Python scripting experience for Scheduling and Process Automation.
• Implemented the machine learning algorithms using python to predict the quantity a user might want to order for a specific item so we can
• Used Spark SQL for Scala & amp, Python interface that automatically converts RDD case classes to schema RDD
• Developing dashboards and visualizations to help business users analyze data as well as providing data insight to upper management with a focus on Microsoft products like SQL Server Reporting Services (SSRS) and Power BI.
• Performing the migration of large data sets to Databricks (Spark), create and administer cluster, load data, configure data pipelines, loading data from ADLS Gen2 to Databricks using ADF pipelines.
• Creating various pipelines to load the data from Azure data lake into Staging SQLDB and followed by to Azure SQL DB
• Creating Databrick notebooks to streamline and curate the data for various business use cases and also mounted blob storage on Databrick.
• Utilized Azure Logic Apps to build workflows to schedule and automate batch jobs by integrating apps, ADF pipelines, and other services like HTTP requests, email triggers etc.
• Worked extensively on Azure data factory including data transformations, Integration Runtimes, Azure Key Vaults, Triggers and migrating data factory pipelines to higher environments using ARM Templates.
• Ingested data in mini-batches and performs RDD transformations on those mini-batches of data by using Spark Streaming to perform streaming analytics in Data bricks.

Worked on Azure Data Factory to integrate data of both on-prem (MY SQL, Cassandra) and cloud (Blob storage, Azure SQL DB) and applied transformations to load back to Azure Synapse.
Managed, Configured and scheduled resources across the cluster using Azure Kubernetes Service.
Monitoring Spark cluster using Log Analytics and Ambari Web UI.
Transitioned log storage from Cassandra to Azure SQL Dataware house and improved the query performance.
Involved in developing data ingestion pipelines on Azure HDInsight Spark cluster using Azure Data Factory and Spark SQL.
Worked with Cosmos DB (SQL API and Mongo API).
Extensive Shell/Python scripting experience for Scheduling and Process Automation.
Implemented the machine learning algorithms using python to predict the quantity a user might want to order for a specific item.
Used Spark SQL for Scala & amp, Python interface that automatically converts RDD case classes to schema RDD.
Developing dashboards and visualizations to help business users analyze data as well as providing data insight to upper management with a focus on Microsoft products like SQL Server Reporting Services (SSRS) and Power BI.
Performing the migration of large data sets to Databricks (Spark), create and administer cluster, load data, configure data pipelines, loading data from ADLS Gen2 to Databricks using ADF pipelines.
Creating various pipelines to load the data from Azure data lake into Staging SQLDB and followed by to Azure SQL DB.
Creating Databrick notebooks to streamline and curate the data for various business use cases and also mounted blob storage on Databrick.
Utilized Azure Logic Apps to build workflows to schedule and automate batch jobs by integrating apps, ADF pipelines, and other services like HTTP requests, email triggers etc.
Worked extensively on Azure data factory including data transformations, Integration Runtimes, Azure Key Vaults, Triggers and migrating data factory pipelines to higher environments using ARM Templates.
Ingested data in mini-batches and performs RDD transformations on those mini-batches of data by using Spark Streaming to perform streaming analytics in Data bricks.

Designed and setup Enterprise Data Lake to provide support for various uses cases including Analytics, processing, storing and Reporting of voluminous, rapidly changing data.
Responsible for maintaining quality reference data in source by performing operations such as cleaning, transformation and ensuring Integrity in a relational environment by working closely with the stakeholders & solution architect.
Designed and developed Security Framework to provide fine grained access to objects in AWS S3 using AWS Lambda, DynamoDB.
Set up and worked on Kerberos authentication principals to establish secure network communication on cluster and testing of HDFS, Hive, Pig and MapReduce to access cluster for new users.
Performed end- to-end Architecture & implementation assessment of various AWS services like Amazon EMR, Redshift, S3.
Implemented the machine learning algorithms using Python to predict the quantity a user might want to order for a specific item so we can automatically suggest using kinesis firehose and S3 data lake.
Used AWS EMR to transform and move large amounts of data into and out of other AWS data stores and databases, such as Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB.
Used Spark SQL for Scala & amp, Python interface that automatically converts RDD case classes to schema RDD.
Import the data from different sources like HDFS/HBase into Spark RDD and perform computations using PySpark to generate the output response.
Created Lambda functions with Boto3 to deregister unused AMIs in all application regions to reduce the cost for EC2 resources.
Imported & exported database using SQL Server Integrations Services (SSIS) and Data Transformation Services (DTS Packages).
Coded Teradata BTEQ scripts to load, transform data, fix defects like SCD 2 date chaining, cleaning up duplicates.
Developed reusable framework to be leveraged for future migrations that automates ETL from RDBMS systems to the Data Lake utilizing Spark Data Sources and Hive data objects.
Conducted Data blending, Data preparation using Alteryx and SQL for Tableau consumption and publishing data sources to Tableau server.
Developed Kibana Dashboards based on the Log stash data and Integrated different source and target systems into Elasticsearch for near real time log analysis of monitoring End to End transactions.
Implemented AWS Step Functions to automate and orchestrate the Amazon SageMaker related tasks such as publishing data to S3, training ML model and deploying it for prediction.
Integrated Apache Airflow with AWS to monitor multi-stage ML workflows with the tasks running on Amazon SageMaker.

Interacted with business partners, Business Analysts and product owner to understand requirements and build scalable distributed data solutions using Hadoop ecosystem.
Developed Spark Streaming programs to process near real time data from Kafka, and process data with both stateless and state full transformations.
Worked with HIVE data warehouse infrastructure-creating tables, data distribution by implementing partitioning and bucketing, writing and optimizing the HQL queries.
Built and implemented automated procedures to split large files into smaller batches of data to facilitate FTP transfer which reduced 60% of execution time.
Worked on developing ETL processes (Data Stage Open Studio) to load data from multiple data sources to HDFS using FLUME and SQOOP, and performed structural modifications using Map Reduce, HIVE.
Developed Spark scripts, UDFS using both Spark DSL and Spark SQL query for data aggregation, querying, and writing data back into RDBMS through Sqoop.
Written multiple MapReduce Jobs using Java API, Pig and Hive for data extraction, transformation and aggregation from multiple file formats including Parquet, Avro, XML, JSON, CSV, ORCFILE and other compressed file formats Codecs like gZip, Snappy, Lzo.
Strong understanding of Partitioning, bucketing concepts in Hive and designed both Managed and External tables in Hive to optimize performance.
Developed Impala for manipulating the data according to Business Requirements.
Developed ETL pipelines in and out of data warehouse using combination of Python and Snowflakes SnowSQL Writing SQL queries against Snowflake.
Experience in report writing using SQL Server Reporting Services (SSRS) and creating various types of reports like drill down, Parameterized, Cascading, Conditional, Table, Matrix, Chart and Sub Reports.
Used DataStax Spark connector which is used to store the data into Cassandra database or get the data from Cassandra database.
Wrote Oozie scripts and setting up workflow using Apache Oozie workflow engine for managing and scheduling Hadoop jobs.
Worked on implementation of a log producer in Scala that watches for application logs, transform incremental log and sends them to a Kafka and Zookeeper based log collection platform.
Used Hive to analyze data ingested into HBase by using Hive-HBase integration and compute various metrics for reporting on the dashboard.
Transformed the data using AWS Glue dynamic frames with PySpark cataloged the transformed the data using Crawlers and scheduled the job and crawler using workflow feature
Worked on installing cluster, commissioning & decommissioning of data node, name node recovery, capacity planning, and slots configuration.
Developed data pipeline programs with Spark Scala APIs, data aggregations with Hive, and formatting data (JSON) for visualization, and generating.