Is Big Data dead?

What is Big Data?

Big data refers to extremely large and diverse collections of structured, unstructured, and semi-structured data that continues to grow exponentially over time. These datasets are so huge and complex in volume, velocity, and variety, that traditional data management systems cannot store, process, and analyze them.


What are the challenges with Big Data?

  1. Cybersecurity and Privacy. Security is one of the most significant risks of big data. ...
  2. Data Quality
  3. Integration and Data Silos
  4. Data Storage. ...
  5. Lack of Experience.
  6. Data Interpretation and Analysis. ...
  7. Ethical Issues.

Source - https://www.datamation.com/big-data/big-data-challenges/


Tips and Techniques / Big Data

  1. Develop a detailed strategy and roadmap upfront. ...
  2. Design and implement a solid architecture. ...
  3. Stay focused on business goals and needs. ...
  4. Eliminate disconnected data silos. ...
  5. Be flexible on managing data. ...
  6. Put strong access and governance controls in place.


Much has been said about big data over the years. The biggest problems have been with ability to manage, secure, and ingest semi-structure, structured, and unstructured data. Evolution of machine learning (ML) and AI (artificial intelligence) have provided more options for private and public sector to work with structured, unstructured, semi-structured data in ways that was not possible 20 years ago.


Source - https://innowise.com/blog/big-data-trends-2024/


The other challenge was with database ability store semi-structured, structured, and unstructured data through leverage of NOSQL Database:

What is NOSQL Database:

NoSQL databases, or "Not Only SQL" databases, represent a diverse array of database technologies designed to overcome the limitations of traditional relational databases. They are built to handle large volumes of structured, semi-structured, and unstructured data with high performance and agility. Unlike relational databases that use structured query language (SQL) and predefined schemas, NoSQL databases offer a more flexible approach to data storage and retrieval.

1. MongoDB: The Popular General-Purpose NoSQL Database

MongoDB has established itself as the go-to NoSQL database for many developers due to its versatility, scalability, and ease of use.

2. Cassandra: The High Availability and Scalability Champion

Apache Spark Cassandra stands out in the NoSQL landscape due to its robust architecture designed for high availability and scalability. It is a distributed database, meaning data is spread across multiple nodes, ensuring there is no single point of failure

3. Redis: The In-Memory Data Structure Store

Redis , an acronym for Remote Dictionary Server, is an open-source, in-memory data structure store that can be used as a database, cache, and message broker. Known for its exceptional performance and versatile data structures, Redis serves as an indispensable tool in many high-performance applications.

Others Couchbase , @Elasticsearch, Neo4j , HBase, Amazon DynamoDB .

Source - https://loadforge.com/guides/choosing-the-best-nosql-database-a-comparison-of-the-top-5-options or https://www.nobledesktop.com/classes-near-me/blog/top-nosql-databases-for-data-science

Data Lakehouse have provided a modern way to handle big data.

Lakehouse architecture is the perfect blend of a data lake and a data warehouse, offering a unified platform that supports a wide range of data processing and analytics needs. It’s versatile enough to handle both structured and unstructured data. Unlike the old-school setups, lakehouses use open formats like Delta Lake and Apache Iceberg, allowing different tools and platforms to easily access data stored everywhere. This setup tackles the big headaches of data management, like the hassle and cost of juggling multiple environments and migrating data between them.

The real magic? Lakehouse architecture ensures your data remains accessible and usable across many tools without constant copying and moving. And in today’s fast-paced digital world, this seamless access to timely and accurate data insights is a game-changer for business decisions and customer satisfaction. You might say, everyone’s jumping in the lake and the water is fine!

Source - https://www.rtinsights.com/how-lakehouse-architecture-will-make-waves-in-customer-data-management/


We are heading for exciting era when it comes driving better decisions based on data.


Paul Young CPA CGA has deployed over 300 data and AI solutions across industries and geographies for the past 8 years. Paul is also an ESG SME on the ESG data journey as part of the

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