Advanced Data Engineering Services

Kagool’s Advanced Data Engineering Team provides specialist capabilities through Professional Services and also Build Factory models for the Microsoft Azure Platform. Covering everything from Ingestion,CDC, Analytics, Report Delivery, Automation, AI & Machine Learning - we have the qualified Data Engineering talent with extensive Manufacturing, Logistics & Supply Chain experience to help you accelerate leveraging Insights from your Landscape Big Datasets.

Data engineering involves the the design, development and management of information or "Big Data" IT landscapes. Data Engineers develop the architecture that helps analyse and process data in a way for the enterprise to generate meaningful actionable insights from it.

Data engineers are part of a larger organisational team which includes business and IT leaders, middle management and front-line employees. The goal is to leverage both internal and external data - as well as structured and unstructured data - to gain competitive advantage and make better business decisions.

Data Engineers manage the operational tasks to support the following activities critical for analytics including: Data Ingestion, Data Synchronization (CDC), Data Transformation, Data Models, Data Governance, Performance Optimization, Production Orchestration.


Ingestion

This is the task of getting data out of source systems and ingesting it into a data lake. A data engineer would need to know how to efficiently extract the data from a source, including multiple approaches for both batch and real time extraction as well as needing to know about both standard connections like JDBC as well as high-speed proprietary connections like TPT. They would need to know about how to deal with issues around incremental data loading, fitting within small source windows and parallelisation of loading data as well for performance consideration.


Synchronisation

This could be considered a subtask of Data Ingestion, but because it is such a big issue in the big data world since hadoop and other big data platforms don’t support incremental loading of data, it is listed it separately. Here the data engineer would need to know how to deal with how to detect changes in source data (CDC), merge and sync changed data from sources into a big data environment. Kagool offers a solution for this called Velocity.


Transformation

The "T" in ETL and focuses on integrating and transforming data for specific usecases. The primary skillset is knowledge of SQL and may involve other technologies also such as Python.


Governance

Data engineers are not responsible for the governance of the data, they do however need to ensure the systems that are needed for data access control and data lineage, are put in place and support the capabilities that are required for good data governance. When a data engineer implements a solution for data ingestions, sync, transformation and models, they need to be aware of data governance concepts so the tooling and platform also support the need for good governance.


Performance Optimisation

Anyone can build poorly performing systems, the challenge is to build data pipelines that are scalable and efficient. So the ability and understanding of how to optimise the performance of an individual data pipeline and the overall system are a higher level data engineering skill. For example, big data platforms continue to be challenging with regard to query performance and have added complexity to a data engineer’s job. In order to optimise performance of queries and the creation of reports and interactive dashboards, the data engineer needs to know how to de-normalise, partition, index data models, or understand tools and concepts regarding in-memory models and OLAP cubes.


Production Orchestration

It is one task to build a data pipeline that can run in an experimental sandbox. It is a second, to get that to perform optimally. It is yet another skill to build a system that allows you to rapidly promote data pipelines from prototype to production, monitor the health and performance of those pipelines and ensure fault tolerance of the entire operational environment. Like performance optimisation, this requires a higher level skill that you tend to see in much more senior data engineers.