Data Engineering

Transforming Data into Tomorrow Ready

Every company needs an efficient way to source, interpret and consume data.Evoke provides data architecture, design, and development services for companies to build sustainable and scalable data.

For most enterprises, data estates are a patchwork of legacy applications, cloud-basedsystems, data in spreadsheet, social media data, streaming data from sensors etc. Many places’ data estates are extremely fragmented; it’s why organisations today are datarich but insightpoor.

Evoke Data Engineers helps companies to address their data challenges and help guide business goals based on intelligent insights. Our team analyses structured, semi structured, and unstructured data with the right process, technology and tools and provide complete data lifecycle management services.

Evoke team helps in data modernization to create a modular platform that is extensible, flexible, secure, cost-effective and well-managed and is ready for new data innovations like AI and machine learning. Following is our robust Data Engineering offerings:

Our Services Summary

Evoke provides Data Engineering Services in 3 key areas:

  1. Data Integration/Data Migration
  2. Data Consolidation Services (Extraction, Transformation and loading of Data into Data Lake, Data Warehouse, Data Base)
  3. API Led Migration/API Management Service

Data Integration Service

Evoke 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:

Evoke has done many data and apps integration projects using Informatica Cloud/Power Centre, Boomi, Talend,Mulesoft, AWS iPaaS, Azure iPaas, Python &PySpark etc. Our Engineers have built many data pipelines from data sources to target storage/applications.

Evoke Data Engineers help our customers in developing near Real-time data pipelines to provide decision makers with more current

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

Evoke “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:

  • Evoke 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:

  • Evoke has developed many Data Warehouses for our customers over the years and our engineers have become expert in dimensional modelling.
  • Currently, Evoke 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.

(c) Database to Database migration Service:

Database to database migration is part of our Data Consolidation service where data is migrated from one database to another one as part of the data consolidation effort. There are various kinds of migrations but the 2 important ones that we frequently do are:

  • Big Bang Database Migration
  • Zero-Downtime Database Migration

Examples of some of the tools that we use for database migrations are:

  • Database Migration Workbench
  • Database Migration Service
  • Schema Conversion Tool (creates the migrated schemas on the target database)
  • ora2pg to migrate an Oracle database or MySQL database to a PostgreSQL-compatible schema.
  • SQL Server Migration Assistant (SSMA) for Oracle

API Led Integration and API Management

Our Data Engineers Provides Following API Services For Our Customers:

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:

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