Data Engineer Senior

Name: Rishikesh Goud Kotha

Cell No: +1-469-***-****

Email: ******************@*****.***

LinkedIn: linkedin.com/in/rishikesh-goud-3b1190279

SENIOR DATA ENGINEER

PROFESSIONAL SUMMARY:

Senior Data Engineer with 9+ years’ experience in designing, implementing, and optimizing end-to-end data engineering workflows to support data-driven decision-making using Azure, Databricks, Data Factory, Synapse Analytics, HDInsight (Spark cluster), ML Studio, Stream Analytics, Blob Storage in the Banking, Telecom, Healthcare, and E-commerce domains.

Possesses a strong working familiarity with various Azure cloud components, including Blob Storage, Data Factory, Storage Explorer, SQL DB, SQL DWH, and Cosmos DB.

Exhibits experience in cloud computing with Azure, along with a strong command of prominent big data analytics tools including Hadoop, HDFS, MapReduce, Hive, HBase, Spark, Spark Streaming, Cloud,, Kafka, Flume, Avro, Sqoop, and PySpark.

Demonstrated proficiency in constructing data pipelines for real-time streaming data and data analytics using Azure cloud components such as Data Factory, HDInsight (Spark cluster), ML Studio, Stream Analytics, Blob Storage, and Microsoft SQL DB.

Experienced in development and support, demonstrating in-depth knowledge of Python, SQL, Oracle, SQL, PL/SQL, and T-SQL queries.

Enhanced data quality and lineage tracking in Azure using MarkLogic's data mastering capabilities for a top-tier bank.

TECHNICAL SKILLS:

Big Data Technologies

Kafka, Cassandra, Snowflake, Apache Spark, Spark Streaming, HBase, Flume, Impala, HDFS, MapReduce, Hive, Pig, BDM, Ab Initio, Sqoop, Flume, Oozie, Zookeeper, and Hadoop

Hadoop Distribution

Cloudera CDH, Apache, AWS, and Horton Works HDP

Programming Languages

SQL, PL/SQL, Python, R, PySpark, Pig, Hive QL, Scala, Shell, Python Scripting, and Regular Expressions

Spark components

RDD, Spark SQL (Data Frames and Dataset), and Spark Streaming

Cloud Infrastructure

AWS, Azure, and GCP

Azure Services

Azure Data Lake, Data factory, Azure Databricks, Azure SQL database, Azure SQL Datawarehouse

Databases

Oracle, Teradata, My SQL, SQL Server, and NoSQL databases (HBase and MongoDB)

Scripting&Query Languages

Shell scripting and SQL

Version Control:

CVS, SVN and Clear Case, GIT

Build Tools

Maven and SBT

Containerization Tools

Kubernetes, Docker, and Docker Swarm

Reporting Tools

Junit, Eclipse, Visual Studio, Net Beans, Azure Databricks, UNIX Eclipse, Visual Studio, Net Beans, Junit, CI/CD, Linux, Google Shell, Unix, Power BI, SAS, and Tableau

NoSQL Databases

Apache HBase, Mongo DB, and Cassandra

Distributed platforms

Hortonworks, Cloudera, and Azure HD Insight

Operating Systems

UNIX, Ubuntu Linux, and Windows 00/XP/Vista/7/8

ETL Tools

Talend Open Studio and Talend Enterprise Platform

Methodologies

Agile, Waterfall, and Scrum

Professional Experience

Professional Experience

T-Mobile - Seattle, WA Apr 2022 – present

Senior Data Engineer

Responsibilities:

Developed and implemented data engineering solutions for recommendation engines, leveraging customer data to provide personalized product recommendations and enhance customer engagement.

Collaborated with cross-functional teams, including marketing and sales, to incorporate recommendation algorithms into e-commerce platforms, increasing conversion rates and sales revenue.

Designed and optimized data pipelines to collect and process large volumes of transactional and customer data for price optimization initiatives.

Integrated machine learning models into the pipelines to analyze historical pricing data, enabling dynamic pricing strategies and maximizing profitability.

Implemented data integration processes to consolidate and analyze inventory data from various sources, improving inventory management and reducing stockouts.

Developed customized ETL solutions and real-time data ingestion pipelines using PySpark and Shell Scripting, facilitating efficient data movement in and out of the Hadoop cluster.

Leveraged Azure Synapse Analytics to optimize supply chain processes in the Telecom, improving inventory management and demand forecasting.

Employed Power BI for enhanced business intelligence and data visualization, leveraging Azure Synapse Analytics as the underlying analytics platform.

Mitigated complex data integration challenges by leveraging Azure Data Factory and PySpark on HDInsight, improving consistency across all customer data sources.

Regularly scheduled data refreshes within Azure Data Factory, providing up-to-date data for effective customer segmentation and marketing campaign planning.

Developed resource-efficient strategies in Azure Data Factory to manage extensive customer data, optimizing cost-effectiveness.

Ensured business continuity by implementing Azure Data Factory's fault-tolerance and recovery features, reducing data loss during critical ETL processes.

Leveraged Databricks, Azure Synapse Analytics, and Delta Lake's ACID transaction capabilities to enhance data management pipelines, reducing data-related errors by 80% and ensuring data integrity.

Exploited Databricks, Delta Lake, and Azure Purview to facilitate schema enforcement and evolution, enhancing data reliability across all Telecom operations.

Implemented robust ETL processes with MarkLogic for ingesting telecom network data into Azure Databricks for advanced analytics.

Implemented comprehensive data governance measures within Azure Synapse Analytics to ensure data accuracy, consistency, and compliance .

Designed a robust SSIS and SSRS infrastructure, adept at extracting data from various sources, setting the stage for an efficient reporting environment.

Worked closely with product and marketing teams to translate their business requirements into data-driven solutions using Azure Synapse Analytics.

Leveraged data visualization capabilities in Azure Synapse Analytics, facilitating stakeholders to comprehend complex data insights, aiding informed decision making.

Designed and implemented streaming architectures using Kafka's publish-subscribe model for real-time event processing.

Implemented Azure Data Factory to seamlessly integrate customer activity data from various SQL and NoSQL databases into Azure Blob Storage.

Led data integration efforts from Cosmos DB into Azure Data Lake using Azure SQL, ensuring uniformity and consistency of customer segmentation data.

Published Docker container images using Azure Container Registry and successfully deployed them into Azure Kubernetes Service (AKS), ensuring scalable and efficient containerized deployments for data engineering solutions.

Engineered robust batch ETL pipelines using Databricks and Azure Data Factory, significantly accelerating data processing speed and accuracy for price optimization initiatives.

Established real-time streaming ETL solutions with Databricks and Azure Stream Analytics, providing instantaneous data for effective inventory management, leading to a 30% reduction in warehousing costs.

Collaborated closely with finance stakeholders to understand their specific requirements and translated them into effective data models through data modeling and dimensional modeling techniques.

Spearheaded a groundbreaking Azure-based MarkLogic implementation for a leading telecommunications giant, enhancing real-time data analytics for network optimization.

Leveraged MarkLogic's geospatial capabilities to optimize the geolocation of network infrastructure elements for a telecommunications giant

Optimized data models by applying data modeling and dimensional modeling best practices, ensuring seamless integration of diverse data sources and enhancing the accuracy and efficiency of financial analytics.

Imported metadata into Hive and seamlessly migrated existing tables and applications to work on Hive and Azure, demonstrating expertise in complex data transformations and manipulations using Azure Data Factory (ADF), Scala, and Python.

Orchestrated advanced algorithms for personalization using Databricks SQL, PySpark, and Azure Machine Learning, leading to a 30% surge in customer satisfaction and a 25% increase in upsell opportunities.

Manipulated Databricks SQL and PySpark with Azure Synapse Analytics to execute in-depth customer segmentation, contributing valuable insights that amplified marketing ROI by 45%.

Collaborated closely with cross-functional teams to design and optimize data storage and retrieval strategies, capitalizing on the power of Azure Data Lake to meet the specific needs of customer account data and order purchase data.

Implemented stringent security measures and access controls within Azure Data Lake to safeguard the confidentiality and integrity of sensitive customer information, ensuring compliance with industry regulations and privacy standards.

Engineered a dimensional model for the Data Mart and generated DDL scripts using Erwin, enhancing data structuring and accessibility.

Streamlined data workflows with Alteryx processing engine and SQL for improved efficiency. Automated customer data archival and restoration processes via PL/SQL procedures and Unix script.

Established a scalable Databricks Data Warehouse architecture using Delta Live Tables and Azure Synapse Analytics, driving significant improvements in the company's capacity to process large volumes of data for price optimization.

Utilized Kafka Streams to develop real-time applications for data transformation, filtering, and aggregation.

Utilized T-SQL scripting for seamless data insertion, updates, and deletions within Microsoft SQL Server databases.

Conducted in-depth analysis of query execution plans and identified optimization opportunities to enhance SQL Server performance.

Implemented indexing strategies and query hints to optimize T-SQL queries for improved response times and scalability.

Integrated Kafka with other stream processing frameworks like Apache Flink or Apache Samza to leverage advanced capabilities for complex event processing and stateful computations.

Designed and implemented data models within Power BI, incorporating data from SQL databases, SQL Data Warehouse, and Blob Storage to create a unified and structured foundation for comprehensive business intelligence analytics.

Collaborated closely with stakeholders to gather requirements and translate them into actionable solutions, leveraging Power BI's integration capabilities to provide insightful customer engagement analytics, marketing performance analytics, and real-time metric monitoring.

Optimized the processing and transformation of customer activity data using Azure SQL before loading it into Azure Data Lake.

Utilized Azure Data Factory for efficient data extraction and ingestion from Azure SQL Database and Cosmos DB, maintaining high data freshness.

Deployed Spark jobs on Databricks for comprehensive data tasks, including cleansing, validation, standardization, and transformation in line with use case requirements.Orchestrated the creation of data pipeline flows, scheduled jobs programmatically (DAG) in the Airflow workflow engine, ensuring impeccable support for the scheduled tasks.

Optimized data models using star schema or snowflake schema to enable efficient and intuitive analysis of marketing campaign data and performance metrics.

Conducted thorough data analysis and profiling to identify key dimensions and measures for inclusion in the star schema and snowflake schema designs.

Engineered a sophisticated risk analysis management system by exploiting Databricks SQL and PySpark capabilities, significantly reducing operational risk by 60%.Deployed and maximized Spark capabilities using Scala for expedited data processing, leveraging Data frames and Spark SQL API.

Championed data-driven insights, performing comprehensive impact analyses, recommending, and actualizing solutions to ensure high-caliber, timely client deliverables and data integrity.

Bridged Azure Data Factory with Azure Machine Learning, employing data-driven predictions to enhance customer personalization and segmentation efforts.

Deployed stringent data encryption measures within Azure Data Factory to protect sensitive customer data during ETL operations.

Accelerated ETL pipeline development by harnessing Azure Data Factory's visual interface, enhancing team productivity and reducing time-to-market.

Leveraged Databricks to integrate Delta Lake's ACID transaction capabilities into data management pipelines, reducing data-related errors by 80% and ensuring data integrity.

Regularly monitored, troubleshooted, and optimized data systems within Azure Synapse Analytics to maintain high performance and reliability.

Implemented robust data security measures and ensured regulatory compliance within Azure Synapse Analytics, fostering customer trust and confidence.

Actively mentored junior data engineers, promoting a culture of continuous learning and leveraging Azure Synapse Analytics for team skill development.

Orchestrated advanced algorithms for personalization using Databricks SQL and PySpark, leading to a 30% surge in customer satisfaction and a 25% increase in upsell opportunities.

Manipulated Databricks SQL and PySpark to execute in-depth customer segmentation, contributing valuable insights that amplified marketing ROI by 45%.

Deployed Spark jobs on Databricks for comprehensive data tasks, including cleansing, validation, standardization, and transformation in line with use case requirements.

Developed and implemented predictive modeling for dynamic pricing using Azure Synapse Analytics, fostering revenue growth through intelligent pricing strategies.

Orchestrated optimization of data ingestion, transformation, and storage processes for various data types in the data lake environment with Azure Synapse Analytics, ensuring efficient data analysis.

Liaised with various teams to comprehend their data needs and translated them into data-driven solutions with Azure Synapse Analytics.

Exploited Databricks and Delta Lake to facilitate schema enforcement and evolution, enhancing data reliability across all operations.

Leveraged Azure Data Lake for advanced analytics and reporting, empowering stakeholders with actionable insights and facilitating data-driven decision-making processes.

Executed workflow improvements in data processes using the Alteryx processing engine and SQL. Utilized Alteryx to parse third-party API data for meticulous validation.

Developed and fine-tuned Informatica mappings using BDM/DEI, PowerCenter, IICS to integrate business rules and modifications effectively.

Designed visually appealing and informative dashboards with Tableau. Generated complex reports inclusive of charts, summaries, and graphs, facilitating clear interpretation of findings for the team and stakeholders.

Employed Git for efficient version control, fostering productive collaboration with the Data Engineer team and Data Scientist colleagues.

Effectively utilized Databricks notebooks for interactive analysis, making the best use of Spark APIs.

Automated DI (Data Ingestion) and DL (Data Loading) scripts using Python & Java map reduce, enhancing operational efficiency and data processing speed.

Engineered robust batch ETL pipelines using Databricks and Azure Data Factory, significantly accelerating data processing speed and accuracy for price optimization initiatives.

Established real-time streaming ETL solutions with Databricks and Azure Stream Analytics, providing instantaneous data for effective inventory management, leading to a 30% reduction in warehousing costs.

Orchestrated a dynamic approach for business requirement collection, customizing it based on project scope and SDLC methodology.

Translated legacy oracle SQL/PL-SQL and Microsoft SQL server/T-SQL into big data platform-friendly scripts, employing PYSPARK, SPARKSQL, and HIVE with SAS and Python platforms.

Conducted ETL operations from Source Systems to Azure Data Storage services, utilizing a robust combination of Azure Data Factory, T-SQL, Spark SQL, and U-SQL Azure Data Lake Analytics.

Implemented data transformations and aggregations within the star schema or snowflake schema to enable meaningful analysis of marketing data.

Created and maintained documentation, including data dictionaries and metadata, to provide a clear understanding of the star schema and snowflake schema designs for marketing analytics.

Demonstrated proficiency database design, relational integrity constraints, OLAP, OLTP, Cubes, and database Normalization (3NF) and De-normalization.

Developed Data Stage extraction jobs using SQL server SSIS, loading the extracted data into a staging area as flat files/Data Sets.

Fused Visual Basic code with SAS for dynamic Excel reporting, utilizing SAS add-in for Microsoft products and Dynamic Data Exchange.

Authored efficient data ingestion systems to transfer data from traditional RDBMS platforms like Oracle and Teradata to NoSQL databases such as MongoDB, ensuring data availability in versatile formats.

Masterminded image data processing through the Hadoop distributed system using Map and Reduce functions, resulting in streamlined storage in HDFS.

Managed the configuration of Zookeeper, Kafka, and Logstash clusters for optimized data ingestion and Elasticsearch performance. Leveraged Kafka for seamless live data streaming.

Pfizer, New York, NY Feb 2021– Mar 2022

Senior Data Engineer

Responsibilities:

Developed and implemented data engineering solutions to analyze healthcare data, including electronic health records (EHR), claims data, and medical research data.

Designed and optimized data pipelines for efficient extraction, transformation, and loading of healthcare data into a centralized data repository.

Collaborated with cross-functional teams, including clinical researchers and data scientists, to identify data requirements and develop data models for healthcare analytics projects.

Implemented data integration processes to consolidate and standardize healthcare data from various sources, ensuring data accuracy and consistency.

Accelerated analytics and reporting in the healthcare domain by leveraging the power of Azure Synapse Analytics for real-time insights.

Implemented a robust data warehouse (DWH) setup using Azure Synapse Analytics, enabling scalable storage and efficient data processing.

Orchestrated patch management and maintenance activities for Azure Synapse Analytics, ensuring system reliability and security in healthcare data operations.

Generated historical snapshots of customer data using Azure Data Factory, enabling retrospective analysis and strategic planning.

Transformed raw customer data into structured formats using PySpark on HDInsight via Azure Data Factory, enabling easy interpretation for business intelligence applications.

Facilitated real-time ETL processes in Azure Data Factory, providing immediate data-driven insights for improved customer experiences and segmentation.

Demonstrated a deep understanding of Kafka's core concepts, including topics, partitions, consumer groups, and offsets.

Ensured data reliability and consistency by implementing appropriate data retention and replication strategies.

Spearheaded the design and development of ETL processes using the Informatica ETL tool, contributing to the creation of dimensional and fact files.

Conducted wide, narrow transformations and executed diverse actions such as filter, lookup, join, count, etc., on Spark Data Frames to enhance data quality and relevancy.

Facilitated a collaborative workspace by integrating Delta Live Tables in managing Databricks Data Warehouse with Azure DevOps, providing teams with real-time and batch-processed data for swift and efficient decision-making.

Championed the integration of diverse customer data from SQL and NoSQL databases into Azure Blob Storage, enhancing data accessibility for analytics applications.

Mastered Databricks SQL and Azure Data Factory to build a data infrastructure, significantly improving patient engagement by personalizing healthcare services.

Utilized Azure SQL Data Warehouse and Azure Blob Storage to reduce risks associated with prescription medicine, decreasing adverse events by 20%.

Orchestrated the seamless movement and transformation of customer segmentation data using Azure Data Factory and Azure SQL.

Enhanced data validation processes during ingestion and transformation phases using Azure SQL, ensuring accurate customer profiling and segmentation.

Crafted Python scripts for comprehensive vulnerability assessment of SQL queries, performing SQL injection, permission checks, and performance analysis. Led the data migration from proprietary database to PostgreSQL using custom-developed scripts.

Proactively monitored and fine-tuned SQL Server performance, identifying and resolving bottlenecks and performance issues.

Collaborated with cross-functional teams to understand data requirements and design efficient T-SQL-based stored procedure.

Pioneered a secure, compliant MarkLogic data store for a major healthcare provider, revolutionizing patient record management with dynamic schema support.

Crafted an innovative financial analytics platform on Azure, harnessing MarkLogic's advanced indexing capabilities to empower rapid portfolio analysis.

Employed MarkLogic's bitemporal features to create a time-travel data auditing system for a healthcare consortium, aiding in retrospective analysis.

Implemented efficient data refresh and synchronization mechanisms within Power BI, ensuring that the data remains up-to-date and accurate for real-time analysis and decision-making.

Created visually compelling and interactive dashboards in Power BI, consolidating data from multiple sources to deliver a comprehensive view of business performance and metrics.

Aggregated log data from various servers using Apache Kafka, making it accessible for downstream systems for improved data analytics.

Implemented Azure Synapse Analytics to centrally manage and organize healthcare data, facilitating seamless access, retrieval, and analysis.

Utilized Azure Synapse Analytics for workload isolation, ensuring efficient resource allocation and optimal performance for healthcare analytics and reporting.

Deployed instant alert mechanisms by leveraging Azure Synapse and Stream Analytics, enhancing response time to real-time data changes and potential threats.

Utilized MarkLogic's semantic search to enable intuitive natural language queries for a health insurance provider's customer support system on Azure.

Orchestrated the development of timely decision-making systems for recommendation engines, capitalizing on the real-time data analysis capabilities of Azure Synapse and Stream Analytics.

Spearheaded the adoption of MarkLogic's Data Hub Framework in conjunction with Azure Data Factory for a comprehensive, unified data integration strategy in healthcare.

Integrated machine learning algorithms into Azure Synapse Analytics, empowering proactive healthcare management through predictive analytics.

Documented requirements and existing code for implementation using Spark, Hive, HDFS, ensuring clarity and effective utilization of resources.

Constructed ETL Mapping with Talend Integration Suite to extract data from source, apply transformations, and load data into the target database, enhancing data flow and quality.

Utilized data visualization tools within Azure Synapse Analytics to represent complex supply chain data in a format easily understood by stakeholders.

Performed regular system health checks, troubleshooting, and optimization of data lake systems within Azure Synapse Analytics to guarantee consistent performance.

Upheld stringent data security measures and ensured regulatory compliance using Azure Synapse Analytics, thereby maintaining a trusted data environment.

Utilized the Kibana interface for filtering and visualizing log messages gathered by an Elasticsearch ELK stack. Transformed data from various file formats (Text, CSV, JSON) using Python scripts.

everaged the features of Databricks, Unity Catalog, and Azure Synapse Analytics to design and implement an efficient ETL pipeline, transforming raw clinical data into actionable insights for various departments.

Integrated Azure Data Lake with analytics services and domain-specific tools to unlock the potential of customer data, enabling advanced analytics, data exploration, and machine learning to derive actionable insights and drive data-driven decisions.

Architected and implemented data governance frameworks and best practices to ensure data quality, traceability, and compliance for customer-specific data stored in Azure Data Lake, considering domain-specific requirements and regulations.

Upheld data governance and regulatory compliance by tracking data lineage and maintaining version histories in Azure Data Factory.

Fostered a culture of technical excellence by sharing best practices for leveraging Azure Data Factory and PySpark on HDInsight for efficient ETL processes.

Streamlined batch workloads through Azure Data Lake, accelerating data processing and enabling real-time analytics for enhanced decision-making.

Facilitated information import and export into HDFS and Hive using Sqoop and Kafka, ensuring efficient data transfer and storage.

Created and maintained documentation, including data dictionaries and metadata, to provide a clear understanding of data structures and relationships within the data models developed through data modeling and dimensional modeling.

Conducted performance tuning and optimization activities, utilizing data modeling and dimensional modeling expertise to improve query performance and expedite data retrieval for finance analytics.

Devised advanced Spark applications for meticulous data validation, cleansing, transformation, and custom aggregation. Employed Spark engine, Spark SQL, and Spark Streaming for in-depth data analysis and efficient batch processing.

Harnessed Apache Spark and Python to drive the creation and implementation of state-of-the-art Big Data Analytics and Machine Learning applications, furthering advanced analytics under Spark ML and MLlib.

Expedited customer activity data flow from Azure Event Hub to Azure Data Lake with Azure Data Factory, supporting real-time analytics and customer insights.

Focused on data security and privacy measures during data integration processes from SQL and NoSQL databases to Azure Data Lake using Azure Data Factory.

Orchestrated the movement of cloud and IoT data using Azure Data Lake, facilitating seamless integration and providing a robust data foundation for diverse data engineering projects.

Conceptualized and executed modern, scalable, and distributed data solutions employing Hadoop, Azure cloud services, and hybrid data modeling. Achieved an optimized data infrastructure for enhanced performance and flexibility.

Exploited erwin's data modeling and transformation capabilities to seamlessly integrate the CRM system with other sales and marketing systems.

Leveraged Redshift's Spectrum feature for querying and joining unstructured data in Amazon S3 with structured data in Redshift, achieving comprehensive data insights.

Incorporated Machine Learning NLP to design a predictive maintenance system capable of anticipating equipment failures based on NLP analysis of equipment sensor data.

SVB – San José, CA Nov 2019 – Dec 2020

Senior Data Engineer

Developed and implemented data engineering solutions for customer data management, ensuring accurate and comprehensive customer profiles and preferences.

Designed and maintained data pipelines for integrating customer data from various sources, including transaction systems and external vendors.

Collaborated with cross-functional teams, including marketing and sales, to leverage customer data for targeted campaigns and personalized customer experiences.

Built and optimized data models to customer segmentation initiatives, enabling effective customer targeting and enhanced marketing strategies.

Configured Spark streaming to seamlessly receive real-time data from Apache Flume and utilized Scala to store the stream data in Azure Table and Azure Data Lake for comprehensive data storage, processing, and analytics.

Developed customized ETL solutions and real-time data ingestion pipelines using PySpark and Shell Scripting, facilitating efficient data movement in and out of the Hadoop cluster.

Pioneered the creation and management of data pipelines within Azure Data Factory, driving enhanced customer segmentation and personalized experiences.

Orchestrated Azure Data Factory to ingest data from diverse sources, boosting the completeness of data sets for accurate customer profiling.

Leveraged Azure Data Factory and Azure HDInsight with PySpark to transform large data sets, enhancing data quality for robust business intelligence applications.

Engineered a state-of-the-art Lakehouse architecture using Databricks, Delta Lake, and Azure Data Lake Storage, significantly enhancing the bank's data processing and analytical capabilities.

Deployed Databricks, Delta Lake, and Azure Data Factory for constructing robust ETL pipelines, streamlining the transformation of raw data from various banking sources into actionable insights. .

Ensured data privacy and compliance with banking sector regulations through secure and compliant data handling practices on Databricks, along with Azure Private Link and Azure Active Directory.

Leveraged Spark Scala functions to derive real-time insights and generate reports by mining large datasets. Utilized Spark Context, Spark SQL, and Spark Streaming to efficiently process and analyze extensive data sets.

Orchestrated the smooth flow of customer activity data from Azure SQL Database, Cosmos DB, and Azure Event Hub into Azure Data Lake using Azure Data Factory.

Implemented fine-grained access controls in MarkLogic to

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