Team Miracle was instrumental in the data migration and warehouse operationalization with Azure and Snowflake,

• Implemented a purpose-built retail Data-as-a-Service (DaaS) solution designed specifically to speed and simplify retailers’ journey to holistic
• Migrated data integration flows (ETL/ELT) using Talend for the data acquisition from flat files, SQL Server, and Oracle
• Migration and Data Validation for the Teradata Schema and Objects to Snowflake Schema and Objects
• Converted data types and objects to Snowflake compatibility along with row count, aggregation, and string hashing-based data validation
• Implemented their data lake for structure and unstructured data to the stage before the Data Warehouse
• SnowPipe-based ETL integration with Azure Data Lake Storage to transform and load data into Snowflake
• Implemented real-time data load using Attunity Replicate for CDC loads from source systems to Snowflake in near real-time
• Migrated (Repointing) BI reports and Consumer applications including Alteryx, Tableau, MicroStrategy, and R along with structural validation and data validation for business-critical reports

• Fully operationalized and migrated to modern data platform with Snowflake and Azure
• Ability to independently scale compute and storage (business users, query users, and batch users) for high availability and real-time reports
• Gain cost-efficiency through a pay-as-you-go model, cloud economics, and on-demand analytical compute
• Enabled quicker time to market for innovative new use cases and self-service analytics through Snowflake and Azure Data Lake
• Improved performance - large queries ran 90% faster for medium Warehouse, 96% faster for XL Warehouse
• Better scalability with zero failures across 480+ queries by 50+ concurrent users on Snowflake, compared to 30+ concurrent users on Teradata with several failed queries

• Our solution for this project included creating a framework of data ingestion processes and creating data pipelines from multiple different sources of data going into Microsoft Azure Data Lake as well as creating a data warehouse reporting schema on an SQL server
• The data ingestion framework included the data pipelines, the data connections as well as error handling structures so that any data-related issues could quickly be reported back to the business and a solution could be worked upon
• Microsoft Power BI was used to connect to the SQL server to provide a reporting structure
• The data in the SQL server was refreshed multiple times during the day, whereas in the Power BI reports, some of the reports were running on the batch data in the Power BI while some of the reports were running in the real-time mode
• As part of the project, we created a complete end-to-end AR (Accounts Receivables) analytics platform which helped the customer to have a complete and true vision of their AR
• The customer had plans to implement machine learning to understand and predict in a better way to understand the customer demand and taste shift. Currently, we are in the third phase of the project where we are working on finding those machine learning use cases and utilizing the Microsoft Azure platform to implement the advanced analytics complete solution for our customer

• There were multiple benefits that this project provided to the customer, the first one included a very minimal maintenance cloud environment and a complete end-to-end fully automated data pipeline as well as a database, and data warehouse solution for their reporting needs
• With this analytical solution, their business was able to take action on changing market conditions as well as customer demands faster and effortlessly
• Were able to improve their turnaround time for processing customer applications and hence improve overall customer experience
• Experienced reduced errors in the overall process due to automation
• Allowed their Server administrators to spend their efforts on more value-added tasks rather than on manual maintenance

• Made an initial assessment and discovery session from which we designed and built a data warehouse system on Microsoft SQL Server that allows them to access the data coming from multiple data sources
• Automated data acquisition and transformation using nightly batch jobs and workflows using Informatica Cloud Services
• Designed enterprise data model and schema design which serves their ongoing analytical and reporting needs
• Created a new migration strategy that includes extracting and transforms relevant products only and opening transactions to the new system
• Implemented new number ranges as per the new system configurations

• Centralized data repository for growing business requirements and business units
• Data warehouse solution for analytical purposes using a fully automated environment to extract, transform, and load the data using scheduled jobs

• Initially, we stored all the data received from multiple sources in their SQL Server databases
• Built a solution to detect any anomalies or violations on their data by gathering SQL server
• Built Machine Learning models to perform analytics on the database logs in real-time
• Implemented search and analytics solution on the Lakehouse using Databrick and AWS
• Lakehouse combined the usage of data lake and data warehouse into a single storage layer and uniformity was maintained throughout the organization on any changes in the data as the data is stored in a single location

• Logs can be backtracked using time travel which helped them avoid any unwanted transformations in the data
• Providing end-to-end streaming pipelines allowed them to not only collect data in real-time but also analyze and send alerts
• Storing the data in a single location improved accessibility and easily performed analysis
• Search and visualization service was paired up with the analytics service to help them build quick and effective models and understand unstructured logs better

We provided a solution to support data stewardship and improve the quality of their data.

As a part of the implementation, our team:

• Build a single source data library platform from various data sources and IOT/Sensors
• Set up a full-fledged Data Lake environment for near real-time, real-time, and IOT/Sensors reports and analytics
• Install and configure Informatica EDC, Informatica Axon, and IDQ in Production and Sandbox environments
• Create customer’s Data Catalog, Classification/Definitions, and Governance operational playbook
• Assist with executing data governance use cases and workflows for Informatica EDC and Axon integration patterns
• Built a self-service analytical platform with strict monitoring by data governance, security, and compliance team
• Deliver an E2E value use case, showcasing metadata reverse lineage from reporting to source, the integration of business terms with technical metadata and data quality rules from metadata resources

• Single source of data inventory paved a way for users to use as one database repository for various reports and analytics/forecasting models
• Enhanced customer safety measures and instant public safety security dispatch
• High standards of data quality and data management
• Better passenger forecasting, flight landings, and cargo onboardings
• Seamless data integration with Informatica IDMC and MDM enabled faster systems integration, CRM, governance, and information management
• Unifying the capabilities of data discovery, data lineage, data profiling, data quality, and business glossary creation enabled efficient self-service analytics and data governance

• Establish business rules across unified MDM, data integration, and data quality on a single platform
• Built a solution Re-identify attributes to define MDM 2.0 logical data model for Material, Vendor, and Supplier based on existing fields and considering future data model expansion
• Consolidate duplicate data into trustable data version and define a Golden Template strategy potentially be inherited across all domains
• Build an integration architecture to achieve near real-time data flow and ensure data sync between system of records and system of origin before downstreaming applications data consumption

• Seamless data integration with Informatica MDM resulted in better business relationships, insights, and knowledge management
• Establishing unified rules with utmost accuracy resulted in the availability of up-to-date, trustable data across the organization
• Redesigning key system integration pipelines reduced data silos with enhanced consistency
• Building governance over master dataPre-built connectors enabled quick-decision making through SAP, Ariba, Salesforce, and CFIN integrations
• Improved attribute management enabled efficient handling of materials, vendor and supplier data