Avik Basu Email & Phone Number

Bengaluru, Karnataka, India

Project - FlowServe:• Designing the Architecture for the Project by creating the design by considering the various aspects - the Volume of the data and the Cost factors associated with the Storage of the data and the ease of accessibility of the data for drawing Insights with the Reporting Tools. • The Architecture Design was also approved by the Microsoft Team when we presented the design in a discussion with the Microsoft team along the client. • Preparation of the Database design by studying the data and then preparing the different Dimension tables and the Fact tables and then identifying the columns for each of the tables and then identifying the Primary keys and the Foreign keys for the tables deigned. • Preparation of the Python Notebooks to consume the data from the different html files which are the Input source for the Project and then cleansing the data and then parsing the data and then preparing the Spark data frames and finally inserting the data into the corresponding Apache Spark Delta tables. • Interacting with the Client to understand any specific requirements and getting clarification for the different queries surfaced during the development of the project. • Creation of the Azure Synapse Pipelines to run the Notebooks created to consume the data from the Landing Zone and after many steps of cleansing, parsing and then finally inserting the data onto the Delta tables and scheduling the same.

Chennai, Tamil Nadu, India

Project - Dialmer Trucks North America:The Project deals with ingestion of real time truck sensor data into Azure Data Explorer (ADX) cluster from Azure Event hubs. The data is stored as parquet files in ADLS via Continuous Export from ADX tables to be consumed by Spark jobs to join Fact data with Dimension data and then storing them in Delta tables to be available for Reporting purpose (MS Power BI) and other downstream systems. - Preparation of Spark jobs (Jupyter Notebooks) to read and consume the data from ADLS (Fact data) and after cleansing and selecting the necessary fields and then joining with Dimension table data which is being read in a different Data Frame with Optimization.- After performing the join, the data is written into Delta tables in ADLS after applying various transformations so that it can served for Power BI and ML predictive analytics.Project ADIDAAS:This a Project based on the Agriculture Domain for the Stated Government of Bihar (India).Here the entire Agriculture Operations for the State in being processed - starting from breaking up of theLand as District, Block and Panchayat, then the Agricultural crops, Seasons, the Government Officials, the Farmers, the Land under cultivation, the production, the damage of crops, the Government subsidy, the various Government Projects for the Farmers, the Infrastructure, Storage facilities and also some Entertainment Activity organized by the Government from time to time - the entire Agricultural Operations of the state.• Preparation of data model and designing the table for all modules for the entire Operations. • Preparation of Pl-Sql Stored Procedures for CRUD operations on the table data via API. • Coordinating with the BA, Front-End, API team to design the Front-End screen for the CRUD operations on the Dimension and Fact tables and creating the Pl-Sql Stored Procedure for the same. • Working on the Customer tickets and request - providing Operation Support for the above Systems.

Bangalore

Project - Pine: The Pine Project implements Master Data Management Solutions to obtain the golden copy of the Customer records from the various Data Sources by using the AllSight Tool and the Big Data Eco System.As there were various Source systems providing the CRM data in different formats, thus to maintain consistency avro file format was used as standard and for some of the Source systems pig scripts were run to convert the data into the avro file formats. For validating the data, Hive tables were created over the avro files and HQL queries were run to check the consistency of the data. Then Stream Sets pipelines were run to ingest the data into the HBase. For real-time data, Stream set pipelines were run to extract the data from the Kafka topic and ingest the data into the HBase. Pipelines were also run to run the Match and Merge jobs and finally the golden copy of the data was obtained as per the Match and Merge rules configured in the All Sight tool and was stored into Azure Data Lake Storage (ADLS). For Reporting, Power BI was used and this was achieved by using the SQL Server pools within the Azure Synapse Analytics component.• Running Pig script to pull the data from Terradata & GreenPlum tables and prepare the avro files for Ingestion from various Source Systems.• Ingestion Pipelines (StreamSets) were triggered in batches for consuming the Input source files.• The Pipelines performed various Transformation to cleanse, format, split the data as Org/Person types.• Address Standardization was performed by splitting the Address component and passing the same through AVS component and stitching the standardized address component back to the data via Py-Spark jobs.• The Transformed data was finally ingested into appropriate Hive tables by running Py-Spark jobs.• The Ingested data from the Hive tables were consumed by Py-Spark jobs to run the Match and Merge Process and finally the golden copy of the data was Published for the downstream systems.

Bangalore

Project – ANZ The project is on Banking domain with Australian and New Zealand Bank. Among the various modules ANZ has the MDM module is of significant importance. The MDM module has been implemented to take data inputs from several sources mainly Internet Banking, Mobile Applications and the various data feed from the Banking operations. The input sources provided the data onto the mdm application through a Common Interface Manager which takes up the data coming as per the source system structure and convert the data as per the MDM database structure and provided as a feed to the mdm application. The mdm applications among various functionalities compares and matches then data and sends the data to AOCM for merging of the same. Then the final data feed comes from AOCM as the consolidated form which is consumed by the mdm application. The data is then available to various other components in the banking system.

Bangalore

Project – Otsuka Pharmaceuticals. The Otsuka Pharmaceuticals has implemented the Master Data Management Solutions for their Customers, Products and their Sales Team with MDM tool - Informatica MDM.The Customer data from the various source Systems and Territory Alignment data were received and processed by the ETL Team and then consumed by MDM Application during the running of the Daily Cadence. The data from the Source Systems were received at the specified locations (FTP servers) from where the ETL jobs loaded the data onto the MDM Landing tables. Then the MDM jobs were executed – Staging, Loading and Match and Merge and the golden copy of the data were then published from which the various downstream systems consumed the data. As different types of end users were consuming the data various tickets were raised as part of the daily operations • To ensure that the Daily Load Cycle (Cadence) was Initiated and monitored and Completed.• To notify the Client and the Data Steward Team regarding the completion of the Load Cycle and when they can use the Environment for their activity and also to prepare and provide the Client with the various types of published data files and reports.• In case of problems with the Load cycle - To Identify the cause of the problem and coordinating across different teams to resolve the problem and let the Load Cycle complete its run.• To constantly monitor the various phases of the Data-flow during the running of the Load Cycle.• Working closely with the client and the Onsite team to discuss and resolve the various problems.• Working on the Service Now Tickets to provide solutions to various problems raised.• In case of major enhancement – Code fix due to Bug Fix or Change Request - to develop a Solution and perform a through Testing.• Conducting Interviews for taking in new team members.• Preparing the KT plans to bring the new team members up to speed about the project and the Daily Operations of the project.

Project - Pfizer• Working with the ETL Tool Talend 5.0 – Extracting the data from the various Source Systems and Loading them into the data tables onto the MDM layer.• Working with the MDM Tool Siperian – Configuring it to take the feed from the ETL layer and after going through the Staging, Loading and Match & Merge process, providing the golden copy of the Data.• Worked in Software Testing with Printer Driver, Mobile and Telecom domain testing.• Worked with Software Automation Tools – QTP and Selenium with JUnit, NG, RC and WebDriver.• Went Onsite with Lexmark Printer Driver Testing Project, to learn and train in the Apple – Macintosh Environment and then preparing the Offshore Test Lab to carry out the Mac Testing from Offshore.• Working as an Offshore Coordinator in a British Telecom Testing Project – communicating and coordinating the activities involving over 10 Component Teams across various locations.• Working as a Point of Contact for the entire Offshore Operations for the Pfizer MDM project involving coordinating the activities across the various support teams like the Unix Team, the SAMS Team, the ETL Team, and the Web Service Team to resolve any issues generated in performing the daily activities, weekly maintenance activities, communicating with the client, working with the Release management Team for the deployment of fixes and enhancements of the MDM product. • Preparation and submission of the various matrices on a weekly and monthly basis.• Participating in the Audit process on a regular basis from the Quality Assurance Group.

B. Tech, Pharmacy

Pgdm, Operations Management

Higher Secondary, Physics, Chemistry, Mathematics

Div - I

Secondary, Physics, Chemistry, Mathematics, Biology, Language, History, Geography Etc, 77%