Data Engineering
Our cloud data engineering services are designed to help our clients take full advantage of the cloud while minimizing the complexity and cost of managing their data infrastructure.
What is Data Engineering?
Data Engineering
Dealing with large scale data - Data Lakes, Pipelines & Platforms. Data Engineering helps identify relevant datasets & prepare the data for use in AI solutions to provide valuable insights to the business. Data Engineering accounts for almost 60% of the work in AI solutions. At DIZIAUTOMATION, we help make your data AI-enabled. Take advantage of evolving data sources by automating & building robust data cleansing & harmonization capabilities to feed machine learning models.
Big Data Engineering
Organizations must help their businesses by building robust capabilities to handle the volume, velocity, accuracy & variety of data. Business users should be able to use this data. Given the constraints of processing large amounts of data, data scientists have the opportunity to work with these massive datasets & provide valuable insights for business teams to make decisions. This significantly increases the achievable enterprise value.
Data Engineering Frameworks
Data Curation
Assist with identifying data sources, verify, cleanse, transform & integrate datasets to make them ready for ML model consumption
Data Lakes & Virtual Warehouses
Assist in powering new insights with a high performing Data Lakes.
Data Governance
Provide data governance framework that helps organizations to take care of the data they currently have, get more value from that data and bring high visibility of data to users.
Data Management
Suggest and help to setup data platforms and tools to manage data.
Data Engineering Strategy
Help you to choose between open source and vendor based platforms
Suggest overall data architecture & Big data ecosystem.
Assist in choosing the right Hosting Strategy (On-Premise vs. Cloud).
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Data Lakes
Design and build complex data lakes to continuously deliver AI-enabled data in near real time. Helps set up automated real-time processes for data ingestion & processing.
Data Harmonization
Make your data clean, compatible, comparable and trustworthy, even from different independent sources. Make sure your data is ready for unsupervised learning, supervised learning & advanced analytics.
Data Processing
Handle large amounts of streaming data process and analyze them in real-time or near real-time to generate valuable insights such as customer sentiment, competitor information and real-time trends.
Governance & Security
Democratize data with strong and flexible governance. Deploy a data governance framework that helps your organization manage the data it currently owns.
Knowledge Graph
It helps create complex knowledge graphs using GraphDB, which is the foundation of modern AI systems. A knowledge graph acts like a memory for AI applications (e.g. apps for recommendations and real-time knowledge sharing).
