Hari M Email & Phone Number

●Over 9+ years of professional experience in working with business applications involving Design, Analysis, Development and Implementation. ●worked in a variety of environments, including Cloudera CDH, Azure, and Databricks. I also had experience defining data strategies, gathering business requirements, and coordinating with cross-functional teams while working with data architects..●Extensive experience using Azure cloud services Azure Data Factory (ADF), Azure Data Lake, Azure BLOB Storage, Azure SQL Database, Azure Cosmos Database, EventHubs, Service Bus Queues, Azure Kubernetes service, Azure Databricks (ADB), and Azure Synapse.●Extensive experience using AWS cloud services AWS Glue, Azure Data Lake,AWS S3, AWS Glacier, AWS DynamoDB, AWS Redshift, AWS Aurora, Kinesis, SQS, AWS ECS, EMR, Athena, and AWS Lambda.●Experience on cloud developments, such as platform as a service, software as a service, and infrastructure as a service provider (IaaS, PaaS, and SaaS). ●Expertise with big data and cloud core technologies, including data modeling, data cleansing, data transformation, data ingestion, and data profiling. ●Experience in Big Data processing with Spark ecosystem applications such as Hadoop, Hive, Spark, and data lakes.●Expertise in developing Spark and Hive scripts for massive data transformation and analysis, as well as Python scripts for data analysis using pandas. ●Experience in working with data of different file formats like Text, CSV, Flat files, Json, Avro and Parquet. ●Accumulated solid working knowledge and experience with Time Series, NoSQL, and RDBMS databases, including MongoDB, PostgreSQL, Oracle, MySQL, and Prometheus.. ●Designed development of pipelines and data ingestion applications using Pyspark and Informatica.●Experience working with Jupyter notebooks, Python visualization packages, and building shell scripts to analyze data from various sources. ●Worked on the Databricks infrastructure, configuring clusters and generating and delivering notebooks.●Hands-on expertise with job automation and scheduling technologies such as Airflow (DAGs, Operators, sensors, etc.) for Spark jobs and Python scripts in terms of time and data availability.●Implemented monitoring on production servers in various regions using Prometheus and Grafana visualization tools.