Best Fit Data Lake Architecture for Optimum Analytics

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.

How to Use Data Better to Drive Analytics?

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.

Data Lake for Depth and Breadth

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.

• Integrate Disparate Data – If properly architected, data lakes enable the collection and retention of all types of data, which may include videos, images, binary files, streaming data and more.
• Unlimited Data Import – Being unstructured, you can import any volume of data in real-time into a data lake from different sources and in different formats. This enables quick scaling up too.
• Secure Storage and Cataloging – You can store relational data from sources such as the operational databases as well as data from line of business applications. You can also store non-relational data such as the one from IoT devices, mobile apps, and social media. It lets you crawl, catalog, and index data for better understanding of the data as well as make it secure as per your data governance policies. It is scalable to enable you to handle an increase in the volume of the Big Data.
• Unhindered Analytics – Business analysts, data scientists, and data developers can analyze the data without having to move it to a different analytics system and use any tool and or framework of their choice, be it open-source frameworks such as Apache Hadoop, Apache Spark, and Presto; or commercial tools.
• Powering Data Science and Machine Learning: Data lakes help transform raw data into structured data that is ready for data science, SQL analytics and, also, machine learning with low latency. What’s more, raw data can be retained at low cost for use in analytics and machine learning.

Architecture Matters

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:

• Ensures data richness through storing all kinds of structured and unstructured data from a variety of sources and in multiple formats such as XML, JSON, text, image, audio, video, etc.
• Enables the conversion of unstructured data to structured data for easy use
• Is secure
• Facilitates the use of open source tools to lower costs and allow scalability
• Integrates data strategy to protect existing investments by enabling existing data warehouses to work together
• Is expandable and allows for a variety of use cases for greater and deeper insights using SQL, NoSQL, Excel etc.

We work with analytical tools based on the customer needs including:

• Azure Data Lake Analytics from Microsoft, a distributed, YARN-based cloud data processing architecture with batch processing capabilities
• AWS Cloud-based Analytics offering an integrated suite of services for the quick and secure  building and managing of data lake for analytics with self-service capabilities
• Apache Spark’s Delta Lake functionality, an open-source storage layer that runs on top of an existing data lake, ensures high data integrity with ACID transactions (Atomicity, Consistency, Isolation and Durability) and uses SQL queries on real-time data

Specific Use Cases for Data Lake Architecture

Customer Relationship Management

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.

Improved Innovation

Research and development teams can understand the impact of their hypothesis and fine-tune assumptions to improve outcomes by capturing insights from unstructured data

Increase Operational Efficiency

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/