In January 2018, McKinsey Quarterly published a whitepaper titled “Analytics Comes of Age”.The paper focused on how advancements in AI and advanced analytics, coupled with an explosion of data, were changing the rules of business decision-making.
Today, business leaders are able to seamlessly integrate facts and intuition, to drive strategic and operational decisions.
The overall market size for big data analytics is expected to grow from USD 138.9 billion currently to USD 229.4 billion by 2025 at a Compound Annual Growth Rate (CAGR) of 10.6%, according to MarketsandMarkets.
But CXOs – across sectors – are realizing that the role of the CTO and CIO in designing optimal big data engineering and architecture is becoming increasingly important. It is no longer only about access and availability of data. The key is to design a big data workflow that has both depth and breadth, ensuring real-time insights are captured.
In this blog, we focus on one aspect of big data engineering, which is data lake architecture.
Businesses typically use data warehouses to run queries and generate reports and dashboards to capture trends, patterns, and insights.
A data warehouse is an optimized database storing data that has been cleaned, enriched, and transformed, providing a unified view of enterprise-wide data. It has a clearly defined schema and data structure for structured data that have been extracted from different lines of business or transactional systems. It is particularly useful for operational reporting and analysis that is SQL-driven.
But for businesses, limiting their analytics to structured-only data is an opportunity lost. Unstructured data too carries insights and provides a more accurate picture when combined with structured data. What businesses really need is a data lake that is a storehouse of both structured and unstructured data and therefore provides a wider view with deeper insights.
A data lake is like a superset with structured and unstructured data ingested from a variety of sources such as IoT devices, mobile apps, social media in addition to business applications. Due to the absence of a schema during data capture, it does not have a design or any specific purpose. Therefore, it can be used for a variety of analytics such as big data, search, log, real-time, machine learning, and so on.
For it to be meaningful, data lakes need the right storage, architecture, data governance, and security model.
At Indium Software, a specialized data engineering service provider, we believe that the right architecture is essential to derive value from your data lake.
In our reckoning, the best fit data lake for your data analytics needs would be one that:
We work with analytical tools based on the customer needs including:
A Data Lake can integrate with the data from the organizational CRM platform as well as social media analytics to gain a deeper understanding of user preferences and behaviour.
Research and development teams can understand the impact of their hypothesis and fine-tune assumptions to improve outcomes by capturing insights from unstructured data
An optimally engineered data lake architecture is critical to garner insights from data generated from IoT Devices, NLP-based models, etc. Overall, it is critical to plan for a data lake, especially in scenarios where unstructured data can make a key difference in your decision-making process.
Indium Software, with more than two decades of experience in cutting edge technologies, has the right team and the experience to be able to study the needs of our customers and design the right architecture for garnering meaningful insights. If you would like to leverage our strengths for your benefit, please contact us here: https://www.indiumsoftware.com/inquire-now/
By Uma Raj
By Uma Raj
By Abishek Balakumar