- What we do
- Why Opcito
- About us
Data is the biggest commodity and data engineering is vital to streamline your data and processing techniques in a world where the sources and forms of data are unlimited. Businesses proliferate with the power of data, and collecting raw data, converting it to usable formats, safekeeping, and storing it in easily retrievable forms significantly impacts your decision-making capacity, ultimately boosting your revenue. Our goal with data engineering is to make data accessible to the right people in the right format at the right time so that you can optimize organizational performance that is dependent on the data.
Opcito's data engineers help accelerate innovation and enable your business to make data-backed decisions with the help of automation in data engineering. We design and create customized robust data management infrastructures and manage large pipelines that process data efficiently. Our end-to-end data management services ensure easy management of large data pipelines by efficiently capturing, processing & storing data to enable better analytics that gets the best out of your business. All this is done keeping in mind the sensitivity, safety, and security of data and the systems that process and house it. Our support teams ensure you never face challenges with data handling, and all your queries are resolved with SLA-based approach.
We help in setting up the right data infrastructure by keeping in mind the database and ETL automation needs with cloud, microservices, modern files & data services, and workload optimization for faster data aggregation, analysis and reporting.
Boost the efficiency of your data processing & storage while considerably bringing down costs with better automation, eliminating the duplication of production data by automating data distribution, data segregation, data sanitization, retention, and clean-up management.
Maintain the organization's integrity by keeping your data safe from possible attacks and threats with data protection, network security, and disaster recovery capabilities combined with encryption, pseudonymization, access controls, and automated backup & recovery.
Embrace a data processing culture with data governance and regulatory compliances for controlled data sharing, optimum retention management, user content governance practices, and case assessments. A strong ETL pipeline ensures easy handling of large cloud data volumes.
Regular, automated data quality checks monitor, identify and fix data quality to deliver trusted insights with real-time search indexing capabilities combined with de-duplication practices that enrich the data quality, boost productivity, and help with greater ROI.
Automated data collection, processing, and storage in easily readable formats by creating a standard data structure and scalable data processing, data lakes, and data warehouse setups for easy retrieval and management of data
Build intelligent data pipelines that are cloud-agnostic and interoperable and that help you Extract, Transform, Load or Extract, Load, Transform the data with strong change data capture, data ingestion, and automation techniques
Design and implement analytics solutions with functionalities to address your current data analytics needs and efficient data visualization of your descriptive, diagnostic, predictive, prescriptive, and cognitive analytics
Simplified cloud data storage, processing, and management through efficient data architecture through design optimization and custom data models that work best for your organizational vision and goals
Organizing and compiling data into the best possible database for your requirements with data warehousing and effective fetching from databases with least possible time with data mining
Break down and visualize complex structured and multi-structured data to drive decision-making with simplified multidimensional or large volumes of data to get a micro-level understanding of intricacies
Expand business data architecture and store more data while spending less on resources with dynamic data storage structures, real-time batch processing, and data lake optimization to save time and to perform faster searches