Like any engineer – Data Engineers design and build. In the case of data engineering, what they are building are the pipelines that transport and transform your data into an ideal format for your business needs. Pipelines take data from many disjointed and separate sources and collect them into a Data Lake or a Data warehouse that represents in a uniform way the single source of truth for the enterprise data. All reports depend on the Data Warehouse. Trust is key.
By definition, data engineers use programming languages to build clean, reliable, and repeatable relationships between data sources and databases.
Our engineers focus on the practical application of data collection and analysis for your business.
Our Data Engineers will focus on these three core areas of your business.
System Architecture
Helping to choose the right Data Integration systems or service that will work together in harmony to extract data sources efficiently and assure data delivery & quality for your business.
Programming
We have expertise with the following Database technologies : Snowflake Computing, MySQL, and SingleStore.
We are experts in Dimensional Modeling, Fivetran, Stitch Data, and other online data services.
Our engineers are proficient in languages like SQL, Python, Java, and Scala.
Analytics
Our staff of engineers will ask the right questions to make sure we build a system that grows with you as your business scales up.
Here at Data Sleek we believe in
Data Integration Services
Combining data from different sources and systems to provide users with a single unified source of truth to provide synchronization of data to be utilized by management teams and decision makers.
Dimensional Modeling Expertise
Understanding the steps necessary to transform OLTP models into Dimensional Models for efficient reporting. ( A lot of people don’t know about it at all and it is becoming increasingly important even in job descriptions). (Dimensional Modeling is part of the Data Warehouse Architecture )
Dimensional modelling in data warehouse creates a schema which is optimized for high performance. It means fewer joins and helps with minimized data redundancy. The dimensional model also helps to boost query performance.
Data Purity – We use data dictionary to match your data properly to its origin. It is fundamental as it will help build the queries later in the data warehouse.
If you’re interested in setting up pipelines between your data source and a data warehouse, how to scale reporting solutions, how to re-architect and scale your data, or need help with real time analytics – let’s talk about the solutions you need!
If you’re interested in setting up pipelines between your data source and a data warehouse, how to scale reporting solutions, how to re-architect and scale your data, or need help with real time analytics – let’s talk about the solutions you need!
