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Given the increasing magnitude of distributive frameworks for data processing, organizations encounter colossal amounts of data spread across contrasting and unconnected systems. Moreover, since the endorsement of Software-as-a-Service (SaaS) applications and cloud services, with larger data and varied access patterns, business operations and IT are facing an ongoing discord over the need to share data. Data silos are the result of the increasing challenges of unifying data.

Modern methods of looking at data integration aim to simplify data through flexible self-service data programs that allow access to data from diverse sources and sharing without impeding existing applications.

Data Warehouse

Serving as a central repository of integrated and structured data from multiple and diverse data sources, enterprise data warehouses are a core component of business intelligence. Primarily, they predefined analytics pattern implementation.

Data Lake

An evolved form of data marts, Data Lakes signify a vast amount of data saved for its pre-supposed value for an enterprise. They are integrated, centralized repository areas for structured, unstructured, and semi-structured data collected from diverse sources and lacking a predefined model.

Data Hub

Using a hub-and-spoke approach, data hubs re-index and physically deliver data into a new system. They proactively serve as nodes of control and data sharing—applying protocols and governance over the data that flows across the infrastructure.

Therefore, it centralizes the enterprise’s data that is vital across applications and enables uninterrupted, end-to-end data sharing between varied endpoints. It achieves this by being the main source of trusted governance data.

Key Differentiators

Characteristic

Data Warehouse

Data Lake

Data Hub

Purpose

Analytics for business intelligence

Cost-effective big data storage

Reconciles data into multiple formats to share data to the edges

Data

Historical data that has been structured to fit a relational database schema

Unstructured and structured data from various data sources

Structured data that can be trusted

Users

Designed for professional business and data analysts

Formulated for data scientists

People and apps

Agility

Less agile owing to its fixed configuration

Highly agile and will be configured and reconfigured as required

Tailored for speed and flexibility of sharing across contrasting platforms

Size

Only stores data relevant to analytics

Can expand to 1,000 terabytes, containing all data relevant to the enterprise

Small in comparison to data warehouses and data lakes

Integrated Solutions

Technically, data hubs function as integration services with indexing, discovering, and analytical capabilities. According to Gartner, 85% of investments in traditional data fail to sail beyond its preliminary stages and deliver value and ROI justification primarily because of poor integration. With data stored in fragments all over the system, in multiple sources and silos, the rate of productivity is inevitably slowed down and additionally impedes innovation. On the other hand, in spite of being scalable, Data Lakes are customary to problems like Data Swamps.

In so far as the different data platforms are efficient in their own domains, the challenge of generating a continuous flow of data lies in unanticipated and persistent changes to data that constantly disrupt dataflow. In other words, there are pragmatic trade-offs to each of these platforms when approached individually.

Business came up with data integration strategies like parallel adoption and big bang adoption. However, these integration solutions are predominantly problem-oriented and are limited in their scope. With the inception of Data Hub, data integration gained a goal-oriented solution that aided businesses with agility and profitability.

Integrating these platforms serves as a holistic compliance solution. Data is turned into real-time data by optimizing every element of the data pipeline so that it is made readily available to users, employees, and partners.

Pyramid Consulting offers professional solutions for clients to assist them in making informed decisions regarding data and intelligence practice. Pyramid Consulting delivers a roadmap that evaluates the client’s applications, data, and infrastructure, identifying the best possible solution to meet their unique organizational goals. Pyramid Consulting offers Managed Data Services to help rebuild, consolidate, and bridge silos and isolated systems in the client’s data architecture.

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Sricharan Vadapalli

About the author

Sricharan Vadapalli

Practice Director, Data, Analytics and DevOps

Vandapalli, or “Sri,” as friends call him, helps clients harness the power of data with the latest and greatest analytic technologies. With a background in IT consulting and career guidance, Sri knows how to bring clients from where they are, to where they need to be. Always developing new skills and knowledge sets, Sri hopes to leave a legacy by teaching others to achieve their goals. An author of literature on Big Data and DevOps, and a yoga and meditation instructor, Sri finds joy in public speaking and mentoring peers in his community.

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