As a Data Engineering leader with around 15 years of industry experience, I have had the privilege of witnessing firsthand the tremendous growth & evolution of the Big data industry over the past decade.
In this article, I will share my experiences & insights into the Big data industry & how it has transformed over the past decade.
When I first started working in the Big data industry around 2012, the concept of Big data was still in its infancy. The technologies that we now take for granted, such as Hadoop & Spark, were still in their early stages of development. At that time, Big data was seen as a niche area that was only relevant to a small group of Data scientists & engineers.
However, over the past decade, the Big data industry has exploded in popularity & become a critical component of many businesses and organisations. This growth has been driven by a number of factors, including the increasing volume & complexity of data generated by businesses and individuals, the decreasing cost of data storage and processing & the emergence of powerful new analytics tools and techniques.
In my experience, the Big data industry has undergone three major phases of evolution over the past decade:
The early days: In the early days of the Big data industry, the focus was on building infrastructure and tools to handle large volumes of data. Technologies such as Hadoop and MapReduce were developed to enable distributed storage and processing of data, and businesses began to experiment with new ways of analysing and visualising this data.
The rise of real-time analytics: As businesses began to generate increasingly large volumes of data in real time, there was a growing need for real-time analytics tools that could provide insights and intelligence in near real-time. Technologies such as Apache Spark and Apache Storm were developed to handle real-time data processing and provide faster, more accurate insights into business operations.
The emergence of Machine learning: As the Big data industry matured, there was a growing recognition of the potential of machine learning and artificial intelligence to transform the way that businesses operate. Today, machine learning and AI are critical components of many big data applications, providing businesses with the ability to automate tasks, predict outcomes, and make data-driven decisions.
Throughout these phases of evolution, one thing has remained constant: the importance of data.
In the Big data industry, data is king, and the ability to collect, store, process, and analyse data is critical to the success of businesses and organisations.
As the Big data industry continues to evolve and mature, I believe that we will see even greater advancements in the way that businesses handle and analyse data, leading to new and exciting possibilities for innovation and growth.
Big Data technologies that I started working with in 2012 -
Hadoop (Cloudera distribution)
Map-Reduce
Hive
HBase
Pig
Oozie
HCatalog
Sqoop
Apache Spark (Around 2015)
Big Data technologies that I am currently working related to Data Engineering-
MongoDB
Apache Kafka
Apache Spark
Scala/Python
Amazon Web Services
Snowflake
Metabase
Imply Druid
Apache Flink
CDC (Change Data Capture)
DBT
Apache Airflow
Trino
Jenkins
CI/CD
In conclusion, the past decade has been an exciting and transformative time for the Big data industry.
As a Big data enthusiast, I have had the opportunity to witness firsthand the tremendous growth and evolution of this industry.
While there are always challenges and obstacles to overcome, I believe that the future of the Big data industry is bright & I look forward to continuing to be a part of this exciting and dynamic field!
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