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Engineering with Big Data

Engineering with Big Data
Engineering with Big Data

Engineering with Big Data

The Big Data Engineering team’s mission is to maximise data’s business value by providing a single source of truth in the form of high quality, certified data. Our goal is to make it easier for developers and analysts to build compliant and consistent products by using the right data.

As organisations transition from traditional to modern data architectures, data engineers play a critical role in building data pipelines with new relevant technologies that can scale and run on the cloud.

The foundation for a career in big data is data engineering. There are no good predictions without managed data.

In today’s dynamic and competitive market, every organisation seeks deeper analytics and insights in order to undertake any enterprise-level transformation. Changes to the way an organisation operates, whether it is entering a new market or operating a business model, are defined as enterprise transformation.

Why is data engineering important?

1

Big Data can help you transform your workforce

We live in a disruptive landscape where emerging technologies are redefining the enterprise of the twenty-first century and customer needs are constantly changing. As a result, organisations must do more to prepare their workforce to meet future demands. The ability to develop the necessary competitive capabilities in a complex and uncertain environment is critical to gaining a competitive advantage.

Transitioning to different roles in a technical career necessitates strengthening competencies and skill sets according to the requirements and role. Short-term courses and certifications are frequently used for such advancements. Such programmes concentrate solely on technical or functional areas, ignoring the aspects of competence development required for the specific role.

2

The Verticals of Big Data

We have several teams dedicated to developing data as a product. This begins with the development of large-scale data pipelines to generate foundational datasets that the entire company uses to power everything from the Economic Graph to our intraday business metrics across the company. Aside from that, we collaborate with and provide tooling to enable our partners to quickly meet their own data needs.

3

Integration of Big Data

Our data ecosystem collects and exchanges information with a wide range of partners and systems. This team focuses on securing data integration by developing standard services and libraries with built-in high availability, compliance, and rapid deployment.

The WMAD Data Integration team’s mission is to unify and leverage the Gobblin ecosystem to enable seamless inter-and intra-company data exchange. This team works to create useful libraries, plugins, and extensions for Gobblin in order to streamline and standardise data integration between WMAD and other third-party vendor systems.

As more of our partners make their data available via APIs, we’ve had to contend with an increase in the diversity and velocity of our integrations. Our primary goal in the near future is to efficiently integrate data from a wide range of sources, formats, and protocols using a well-defined interface and robust metadata. We truly believe that a configuration-driven shared service (Generic Connectors) with a common object model and the ability to expand the suite with distinct patterns is the solution.

Other significant integrations are with CRM systems that serve a larger business community and allow them to make critical business decisions. We are currently constructing a privacy store, which will include standardising member opt-outs for third-party integrations. This service integration is part of a larger ecosystem that includes the creation of Kafka events, a Venice Key-Val Store, and a Rest Interface.

Big Data Engineering

Our vision at WMAD is to create economic opportunities for the global workforce. To achieve this vision, we conduct a wide range of applied research on the rich data that flows through our systems. However, leveraging our data effectively necessitates both technical mastery of big data distributed systems and business domain expertise—two disparate fields with little in common. To address this challenge, the Big Data Engineering (BDE) vertical teams propose and develop data products for our data scientists, analysts, and business stakeholders across a wide range of focus areas within the company, from marketing to talent and sales to consumer analytics.

On the one hand, we develop and deploy cutting-edge technologies. Our technology stack is made up of a number of distributed platforms. For large-scale data processing, we use both open-source and proprietary frameworks.

Engineering with Big Data

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