Data Engineering

At VDS, Data engineering secures the groundwork for your data science and analysis, it builds and consolidates your system architecture to ensure that the data you collect is accurate, useful, complete and error-free. 

Our Data engineers  prime the data  and make it accessible for analysis and action; Data architecture plays a leading role here. Our data architecture would be holistic, resilient and future-proof, able to comprehensively deal with different data across business operations via high-quality data lakes and data pipelines.

VDS provides the below services as part of our Data Engineering solutions.

  • Design, develop and test data architectures
  • Optimize Data Architecture for Your and Your Business
  • Discover and Implement New Opportunities
  • Scalability and Modernisation

Data Engineering As A Service

We can transition data from legacy systems into modern systems. We are adept in SQL; even if legacy systems don’t use RDMS databases, modern systems still often use SQL interfaces.

Huge volumes of data have to be coupled with quality system architecture and well-planned use strategies. We can engage your business with the power of big data strategies that drive value and ROI.

Data lakes are centralized pools of data. We can streamline the ingestion of data into data lakes for piping downstream to applications and other endpoints.

Our Data engineers construct both ETL pipelines to move and transform data from system to database and ELT pipelines that transform data at its destination. Data can be moved and transformed via either batch processing techniques or real-time streaming.

We can construct data systems that pipe data into machine learning applications.

Our experts can use your existing data and newly collected data to construct foundational predictive models that can be actioned across your products and services.

Making quality data available in a reliable manner is a major determinant of success for data analytics initiatives be they regular dashboards or reports, or advanced analytics projects drawing on state of the art machine learning techniques. Our data engineers tasked with this responsibility take into account a broad set of dependencies and requirements as they design and build their data pipelines.

Our data experts have years of experience with software engineering. In addition they possess in-depth knowledge in:

  • Coding and testing patterns, 
  • Object-oriented designs, 
  • Working on open source software platforms 
  • NoSQL and data warehousing 

Our Big Data engineers are experienced in building massive big data reservoirs and highly scalable and fault-tolerant distributed systems, that can inherently store and process massive volumes or rapidly changing data streams. They also develop, construct, test, and maintain frameworks like large-scale data processing systems and databases. Once data flow is achieved from these pools of filtered information, our data engineers would then incorporate the required data from their analysis.

VDS’ data engineers are well experienced in:

  • Apache Hadoop: Apache Hadoop has seen tremendous development over the past few years. VDS understands caters to components like HDFS, Pig, MapReduce, HBase, and Hive are currently in high demand. Many software companies are still heavily relying on the clusters due to its ability to deliver perfectly mapped results.
  • NoSQL: At VDS we have engineers with years of experience and expertise in the administration of NoSQL databases like MongoDB and Couchbase. Based on experience, VDS understands that NoSQL databases are better equipped with meeting big data access and storage needs. In addition to this, their data crunching ability also complements Hadoop’s expertise.
  • Setting Up Cloud Clusters: Given the acute reliability that big data places on networks, a lot of work is outsourced to the cloud to avoid the hassle. To accommodate the wide volume of big data, several cloud clusters are set up depending on the organisation’s requirements. Not only does the elasticity offered by cloud makes it ideal for big data engineering, but cloud clusters also make it easier for engineers to crunch large volumes of data to discern patterns. 

Machine Learning: Even though big data engineering has a lot of scopes, machine learning and data mining make an important contribution to the field and are some of its most prominent components. Our engineers can effectively use machine learning for carrying out prescriptive and predictive analysis. Our data engineers have in-depth experience in classification, recommendation, and personalization systems.