5 Technology Enablers for DataOps
Oct 15, 2020 • 7 min read
Oct 15, 2020 • 7 min read
Every company wants to take advantage of their data to turn it into actionable insights. It can be used to build customer 360, reduce customer churn, plan marketing campaigns, and optimize pricing, inventory, and supply chain. Data is also key to increasing productivity and the efficiency of the workforce. In order to manage increasing volume, velocity, and variety of data without sacrificing quality, security, and accessibility, companies need to build a robust analytical data platform.
To reliably generate high-quality insights, augment decision making with AI and ML, and increase the level of business intelligence, the data analytics department needs to follow three steps:
Steps two and three are usually repeated in that new data and insights are continually added and generated over time. But business value is only ever created in Step 3 and is dependent on the quality of the insights that are generated. So while executives want to get to insights as soon as possible, without a robust foundation in the form of an analytical data platform, data analysts, scientists, and engineers will not be productive.
To help companies reduce the cost, effort, time, and risk of building analytical data platforms, Grid Dynamics together with AWS created an accelerator that should satisfy the data analytics needs of small technology startups and large enterprises.
In the article describing the journey from data lakes to analytical data platforms, we analyzed why traditional data lakes do not satisfy the needs of modern data analytics teams. The blueprint of an analytical data platform includes capabilities for easy and secure access to data by analysts and scientists, data governance tooling, stream analytics, data monitoring and quality, enterprise data warehousing, reporting and visualization tooling, as well as AI/ML platforms.
Together, all these capabilities facilitate DataOps and MLOps processes and provide a solid foundation for data scientists, analysts, and engineers to generate business value quickly and reliably.
Companies can use the analytical data platform accelerator in three different cases:
No matter what the starting point, the analytical data platform accelerator built by Grid Dynamics and AWS can provide the following benefits:
The analytical data platform is built with AWS cloud services, open source components, and includes other Grid Dynamics accelerators for data quality, data monitoring, and anomaly detection. It has a modular structure, so companies don’t have to provision the entire platform. Although the core accelerator is industry-agnostic, it contains two sample AI/ML use cases from retail industry, which demonstrate the end-to-end data and ML pipeline, image recognition for automatic product attribution, and promotion planning.
The high-level architecture of the accelerator is outlined in the diagram below:
The following capabilities of the analytical data platform are implemented as separate modules and can be provisioned independently:
Following enterprise cloud best practices, different parts of the accelerator can be provisioned in separate VPCs or cloud projects.
This is an example of a single cloud project installation:
The easiest way to get started with the analytical data platform is by using the AWS Service Catalog. In advanced cases, a company may decide to provide the platform via a self-service portal. ServiceNow, Jira, or custom portals can be integrated with the AWS Service Catalog to orchestrate provisioning of necessary capabilities and AI use cases, guaranteeing compliance with the internal security and IT policies.
In the next article we’ll provide a step by step getting started guide for provisioning the solution using the Service Catalog.
Grid Dynamics offers services to plan, design, prototype, integrate, and implement the analytical data platforms accelerator in the client ecosystem, along with onboarding of batch and streaming data sources, migration of data and platform components from on-premise to the cloud, and implementation of required AI/ML use cases. The typical engagement models can be found below:
Building an analytical data platform can be a daunting and difficult task. Most companies want to get to insights as soon as possible and avoid investing too much time and effort into the foundational capabilities. To address this need, Grid Dynamics created a pre-integrated accelerator for the modern analytical data platform and published it on AWS service catalog. We are excited about this new solution and welcome everybody to use it to increase their speed to market and reduce the risk and effort of implementing the platform from scratch. If you are interested in a demo or would like to explore how the accelerator can help you, please reach out to us.