Transforming Data into Tomorrow Ready
Our Services Summary
Richesmtech provides Data Engineering Services in 3 key areas:
- Data Integration/Data Migration
- Data Consolidation Services (Extraction, Transformation and loading of Data into Data Lake, Data Warehouse, Data Base)
- API Led Migration/API Management Service
Data Integration Service
Richesmtech has 19 years of extensive Data Integration experience using various tools, frameworks & platforms. Our integration team is experienced in cross-platform integration, from on-premise to Cloud or vice versa.
Some of the key tools/framework/platforms that we have been using for data integration are listed below:
data. Our Modern data pipelines approach rely on the cloud to enable customers to automatically scale compute and storage resources up or down. Usually for Modern data pipelines, we design a distributed architecture that provides immediate failover and alerts users in the event of node failure, application failure, and failure of certain other services. It also has advanced checkpointing capabilities that ensure that no events are missed or processed twice.
Data Consolidation Service
Richesmtech “Data Consolidation” is the classic data integration process leveraging ETL(extract, transform and load) which helps in combining data from disparate sources, transforming & aggregating after removing its redundancies within a single data store like a Data Lake OR Data Warehouse OR Database.
(a) Data Lake Implementation:
- Richesmtech has helped our customers in building their Data Lakes on Cloud OR On-Prem servers which involved multiple ELT processes. Evoke has expertise in implementing Data Lakes on:
(1) AWS
(2) Azure
(3) Google
(4) Databricks
(5) Snowflake.
(b) Data Warehouse Implementation:
- Richesmtech has developed many Data Warehouses for our customers over the years and our engineers have become expert in dimensional modelling.
- Currently, Richesmtech is involved in two Data Warehousing implementations. Out of which one is very large consisting of 400 + ETLs,30 DataMart’s using 30 + Source Systems,900 Tables,190 Views. We are doing this for one of the largest providers of Registered Agent, UCC search and filing, compliance, and entity services.
- Our Data Engineers are very experienced in developing Cloud based Data Warehouses like in AWS/Azure/Snowflake. Because traditional enterprise data warehouses are experiencing huge data explosion, data latency and increasing cost hence many customers are moving tomodernized cloud data warehouse. As part of this, modernized cloud data warehouse can handle high volumes of structured and unstructured data using agile methodology to unify disparate data sources in customer’s technology ecosystem.
- We are also working on an Azure based Data Warehouse for a large Organic farms company in USA. There are around 50 Fact tables and 30 Dimensions built in the Data Warehouse with around 200 Data Factory Pipelines(ETLs). Azure Data Factory,Azure Synapse Analytics(dedicated SQL Pool), Azure SQL, PowerBI etc. are used in this implementation.
API Led Integration and API Management
We are using various API tools as part of our Data Integration and Application integration effort in various projects.
Below is a list of the key API tools that we usually use for our customers: