Transform your business data into insights
Transform your business
data into insights
With growing needs of analysing vast scales of data, Big Data Engineering services have emerged as a separate stream along with Data Science. While Data Science deals with generating insights from data, Data Engineering deals with managing and preparing the data for business analysis.
Data Engineering was originally limited to data management on traditional data platforms like RDBMS, Datawarehouses (DW), Datamarts, etc,. Data Architects designed and managed the data models, data governance, master data management and data security.
ETL Engineers used to manage data pipelines and Data Analysts mostly generated reports using basic SQL and reporting tools. The statisticians ran models on the traditional data sets. In the last decade or so, the growth of data volume has become exponential in most of the industries.
Traditional ETLs, Databases and statistical models couldn’t handle the volumes. Along with volume, there is an increasing need for analytics on variety of data, real-time data and quality of data. This triggered the need for Big Data platforms and Data Engineering for Big Data.
- Create Reference Architecture Blueprints
- Create a Factory Design for migration
- 12-Factor App Readiness
- Application and Infrastructure Monitoring
- Build a common authentication bus
- DevOps with CI/CD and Release Automation
- Canary deployments or Blue/green deployment
- Policy-based security orchestration for containers and services
- Solutions for accessing traditional storage with object storage overlay with tools such as MinIO
- Data Competency in Hadoop with focus on Hortonworks and Cloudera
Shimento’s Approach to Big Data!
Strategy and Architecture
Your enterprise is unique in its data requirements and goals. In order to implement the right data solutions, you must first analyze your existing data systems. That’s where Shimento comes in. Based on the results, we create a plan and process for migration to big data technologies, or a hybrid of traditional and big data architectures, to aggregate and transform your data into actionable information
Infrastructure Sizing and Setup
To provide comprehensive infrastructure management, we leverage Hadoop to build modern data ingestion platforms using data lakes and data warehouses on platforms like Hive and Impala.
Data Processing Architecture
You’ll need an architecture for tightly integrating real-time and batch mode data processing. Shimento’s best practices employ Lambda architecture and backend NoSQL data store for real-time processing. For social data, we leverage real-time platforms such as Spark Streaming
Master Data Management
If your data management initiatives are a series of successes and failures, Shimento can change that trajectory. We bring decades of experience overcoming the challenges of integrating master data management and NoSQL. Using clustered setups and high-performance, scalable lookups, we deliver the benefits of high-performance transactions to your door.
Data Integrations and Plugins
Your IT experts can attest to the nuisances this has caused over the years. To solve these issues, Shimento’s engineers developed our own IP technology. These accelerators provide a rich set of built-in, device ready, technology-agnostic functions to help you become a dynamic, data-driven enterprise
As a business or IT leader, you’re always looking to extract more value from your cloud investments. The way to increase that ROI is to prioritize workloads that can exploit the wealth of services and capabilities offered by your cloud provider, with increased focus on asset utilization to control costs