Data Engineering
Is your data out of control?
Learn how data engineering helps you successfully solve your company’s data management challenges.
Common Challenges in Data Engineering
Since data engineering projects are gaining popularity and use cases are growing in complexity, there are a few challenges that teams may encounter along the way:
Data growth and
storage issues
Difficulty integrating multiple data sources
Support and maintenance
of ETL processes
Lack of proper understanding of massive data
Accessing and sharing
data issues
Lack of data governance and management
Unclear
Data Strategy
Performance and Scalability issues
Data Processing
errors
If your company lacks a fundamental data engineering strategy, the data that is collected is essentially useless.
DAECO's data engineering model allows you to successfully face these challenges
The Data Pipeline in Daeco
How is our data engineering process?
01 – Ingestion
02 – Processing
During this phase, ingested data is sorted to achieve a specific set of data to analyze. For large data sets, this is commonly done using a distributed computing platform for scalability.
03 – Storage
This takes the results of the processing and saves the data for fast and easy retrieval. The effectiveness of this phase relies on a sound database management system – which can be on premise or in the cloud.
04 – Access
Once in place, the data is available to users with access.
Data engineering is the process of designing and building systems that let people collect and analyze raw data from multiple sources and formats.
What will you get by leveraging DAECO's data engineering?
Governance – Management – Security – Scalability – Confidentiality – Support
A comprehensive data
management strategy with a data governance plan.
Aligning data collection methods and bringing the data to one central location.
Maintaining and upgrading the data architecture.
System integration in every department of the enterprise.
Monitoring and managing all the data systems.
Increases the knowledge
of the business domain.
Designing and developing data architecture based on the existing business systems.
Improving the quality of collected data and structuring it.
Optimizing the data architecture to solve the intended business problems.
Unlock the power of big data and reap the benefits sources and formats.
Become a data-driven business
And accelerate your digital transformation with enterprise data management for immediate business decisions.
