Redefining modern data stacks: What’s hot, what’s not

Data – while it’s the most critical asset for any business to propel its growth, leveraging its true analytical value without a modern solution can be daunting. A few data-driven enterprises have already embraced modern data stack trends in AI/ML and BI, while others don’t want to miss out on riding the modern data wave and be laggards. 

Staying competitive in today’s fast-paced business landscape requires a modern data stack that can keep up with your data-driven needs. Discover the essentials of a modern data stack and the crucial things to consider before its strategic implementation in our upcoming fireside chat. Join experts from Forrester and Gathr as they provide insights on the latest trends and technologies that make up a state-of-the-art data stack, as well as real-world pragmatic approaches for its implementation.

The modern data stack is designed for elasticity and scalability. The platforms decouples compute, storage, and the state of the data making it possible to support a wider variety of domains and use cases. Organizations are no longer constrained to monolith and bespoke warehouse implementations. Instead, the platform supports centralized, decentralized, and federated workloads. In addition, modern data engineering practices are supported. Data engineers are able to build data products to compose solutions quickly, have interoperability across systems and use cases, and portable across platforms and environments.

The technical skill bar is lowered. Data engineers to data consumers have no-code/low-code tools to create, deploy, manage and share data pipelines, data sets, connectors, and ML models. However, the modern data stack still supports the use of opensource and code for more sophisticated used and solutions. Self-service is supported by data catalogs to find, access, publish, and managed data products and assets. And data governance is baked into the platform for wider democratization of data and insight.

More organizations are decentralizing their data development and analytic efforts. A. modern data stack is better prepared with scalable infrastructure for an enterprise foundation, and zones for business unit needs. The data estate handles analytic and operational workload while taking advantage of a data fabric designed for multiple batch and real-time data processing and speeds. Data governance is better supported by relinquishing top down policies and controls in favor of federated governance to deploy global policies but iterate and use local or use case specific policies. Cost is another benefit. With modern data stacks running in the cloud, organizations can do more with less, scaling to meet more data producers and use cases.

Lift and shift is a common mistake during migration. For those deploying a new cloud environment, lift and shift increases cost due to poor configuration and optimization of workloads. Thus, cost benefits are unrealized. In addition, more attention is needed to orchestrate and coordinate data consistency between data sources. This is especially true as data is more virtualized to create data sets or satisfy queries and requests. Data flow between data sources, centralized cloud databases, and cache is designed for the consumption scenario, not a centralized data warehouse. In addition, data governance is built into the platform and not an afterthought. Data governance is overwhelmed due to the distributed nature of data and wide selection of use cases and domains supported if applied post migration. Iterate the MDS deployment by use cases and domains to avoid these pitfalls.

MDS by nature is built for iteration. MDS expects continuous evolution of existing use cases and the addition of new ones. Ideally, enterprise data management teams should build a foundation and core of enterprise data, data connectivity, and data policies as a launchpad. Data engineers, data scientists and analysts, data stewards, and data consumers build on top of the foundation. This ensures new data, new data policies, and connections and integration with other sources and systems is purposeful and contextual. Ultimately, this leads to stronger and tangible data monetization for the enterprise.

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