Authors :
Venkata Ramana Reddy Bussu
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/4wfvbah6
Scribd :
https://tinyurl.com/522jywwu
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR166
Abstract :
Databricks, as a unified analytics platform, has
emerged at the forefront of this evolution, offering
scalable cloud-based solutions for data science and ML
applications. This article explores the journey of
Databricks in enabling data-driven decision-making
through advanced analytics techniques. From its roots in
Apache Spark to its current status as a leading platform
for data engineering, data science, and machine learning,
Databricks has continuously evolved to meet the growing
demands of modern enterprises. This article examines the
progression of data science/Machine Learning
applications in Databricks, tracing their development
from initial implementation to current state-of-the-art
techniques and integration within the platform. Initially,
the article delineates the inception of Databricks, focusing
on its architecture and the early adoption of Apache
Spark for big data processing. It explores how the
platform's native support for various programming
languages and its unified analytics engine facilitated the
early stages of intelligent application development. The
article further discusses the implications of these
advancements for the future of data science and
Intelligence within Databricks and the broader analytics
ecosystem. It highlights the potential for further
integration of AI and ML technologies, such as automated
machine learning (AutoML) and real-time analytics, in
enhancing decision-making processes and operational
efficiencies across industries. The evolution of data
science in Databricks has played a pivotal role in
advancing big data analytics, offering scalable, efficient,
and user-friendly solutions. This study not only charts the
historical development of these applications within
Databricks but also provides insights into future trends
and potential areas for innovation. As data continues to
grow in volume and complexity, platforms like Databricks
will be instrumental in harnessing the power of data
science and ML to drive insights and value across sectors.
Keywords :
Databricks, Apache Spark, Data Science, Machine Learning, Unified Analytics, Big Data, Data Engineering, Artificial Intelligence.
Databricks, as a unified analytics platform, has
emerged at the forefront of this evolution, offering
scalable cloud-based solutions for data science and ML
applications. This article explores the journey of
Databricks in enabling data-driven decision-making
through advanced analytics techniques. From its roots in
Apache Spark to its current status as a leading platform
for data engineering, data science, and machine learning,
Databricks has continuously evolved to meet the growing
demands of modern enterprises. This article examines the
progression of data science/Machine Learning
applications in Databricks, tracing their development
from initial implementation to current state-of-the-art
techniques and integration within the platform. Initially,
the article delineates the inception of Databricks, focusing
on its architecture and the early adoption of Apache
Spark for big data processing. It explores how the
platform's native support for various programming
languages and its unified analytics engine facilitated the
early stages of intelligent application development. The
article further discusses the implications of these
advancements for the future of data science and
Intelligence within Databricks and the broader analytics
ecosystem. It highlights the potential for further
integration of AI and ML technologies, such as automated
machine learning (AutoML) and real-time analytics, in
enhancing decision-making processes and operational
efficiencies across industries. The evolution of data
science in Databricks has played a pivotal role in
advancing big data analytics, offering scalable, efficient,
and user-friendly solutions. This study not only charts the
historical development of these applications within
Databricks but also provides insights into future trends
and potential areas for innovation. As data continues to
grow in volume and complexity, platforms like Databricks
will be instrumental in harnessing the power of data
science and ML to drive insights and value across sectors.
Keywords :
Databricks, Apache Spark, Data Science, Machine Learning, Unified Analytics, Big Data, Data Engineering, Artificial Intelligence.
