DATA ENGINEERING
The volume of data has grown dramatically over the years, and organizations have realized that insights from data are their biggest assets. Despite the emergence of Big Data Analytics, AI & Machine Learning, and other modern technologies, traditional data warehouse, business intelligence and reporting constitutes a major chunk of workloads. Whether it is retail, insurance, finance, or manufacturing, DW/EDW and BI remains a powerful tool for data analytics, reporting, and visualization. Optigrise follows a unified approach to data engineering using unified tools and processes, extending DevOps & Agile principles to data
Typical Approach
- Solid Approach – Separate tools, process for different teams
- Separate Pipelines – Separate pipeline/data flow b/w traditional data engineering, big data & ML teams.
- Focus on data science only – While AI and predictive analytics solve many use cases, still organisations have huge amount of data in relational and structured form, they should continue to have a strong DW/BI strategy.
Our Approach
- Unified Approach – Unified tools, process
- Unified Pipelines – Unified pipeline from data ingestion, data preparation to visualization for traditional DW/BI, Big data & AI.
- Balanced Approach – Balanced approach b/w traditional DW/BI, Big data & AI.
- Data Ops – Bringing in Devops & Agile Principles to data projects.
- Cost Optimization – Cost saving on DW/BI, so that additional savings could be spent on AI & Big data.
Our Areas Of Expertise Include
Relational Databases
Expertise in relational data bases like SAP HANA which is also an in memory database and Microsoft SQL Server & Azure SQL Database, a new generation relational database that has a ton of features for semi structured and unstructured data. Others areas of expertise are MySQL, PostgreSQL and AWS Aurora.
Data Warehouse and Business Intelligence
Proficiency in Terradata, a new gen NewSQL engine improving query performance at scale with built in Big data, AI and graph analytics engines. Our technical know-how also extends to SQL Server DW, Azure SQL Data warehouse, SAP Data Warehouse, Snowflake, AWS Redshift, and IBM BigQuery.
ETL/ELT & DataOps
Traditional ETL is not real time and cannot scale to cope up with the growing data volume. It also creates bottlenecks in the pipeline causing an Integration spaghetti. Streaming and Messaging based systems like Kafka or Kinesis could solve these problems. Using a pub sub based architecture removes the point to point Integration spaghetti.
Business Intelligence, Reporting & Visualization
Our competence in BI include Tableau, Qlickview, Power BI, SSRS, FusionChats, D3.js.
NoSQL Databases
Expertise in Document Database, Key Value Stores, Wide Column Database, In memory database, Graph Database, Time series database etc.
Big Data & Analytics
Well experienced in frameworks like Hadoop – HDFS/Map Reduce, Azure Data Lake – Storage & Analytics, Azure HDInsight, AWS EMR, Databricks & Delta Lake. We also provide services in Data Security, Privacy & Compliance, following General Data Protection Regulation (GDPR) guidelines, and other regulations concerning the protection of data with the right approach to Data Engineering, organizations can monetize and maximize the value of their data assets. Leverage our full array of data engineering services and transform your company’s data into actionable intelligence.
Data Engineering
Despite the emergence of Big Data Analytics, AI & Machine Learning, and other modern technologies, traditional data warehouse, business intelligence and reporting constitutes a major chunk of workloads
DW/BI
Despite the emergence of Big Data Analytics, AI & Machine Learning, and other technologies, data warehouse and business intelligence remain a powerful tool for data storage, semantic modeling, analytics, reporting, and visualization.
NoSQL
Relational databases are ideal for structured data that could be stored in row and column format. However, much of the data today are semi-structured and unstructured that do not fit well in these relational databases.
DevSecOps
At Optigrise, we approach DevOps as an aggregation of Agile Engineering principles. We also look at DevOps as the next iteration of Application Lifecycle Management (ALM).
Product Engineering
Product Engineering is the process of innovating, designing, developing, testing and deploying a software product. Software Products have undergone a sea of change in last few years.
Proof of Technology
Well suited for Mainframe & Client-Server applications, and even for first gen Internet/Web applications where applications are cloud native, uses micro-service & containers for fast development .
Engineering Services
Optigrise’s engineering services & solutions help design, develop, and maintain custom applications & platforms to drive your business using emerging technologies.
Performance Engineering
Well suited for Mainframe & Client-Server applications, and even for first gen Internet/Web applications where applications are cloud native, uses micro-service & containers for fast development .