No-Code Data Lakehouse is Trending

You must have heard the term “Data is the new oil”, but how do we mine, refine and get the best value from the most important asset to business growth and continuity? In search of answers, Data management is evolving. Today, Data Lakehouse is trending.


Let us go back to the basics of how we handled data– Data Warehousing.


Data warehousing was built basically for Business Intelligence and reporting but with no support for varied data types like video, and audio. No support for Data Science, Machine Learning, and limited support for real-time streaming.


In Gartner’s study, more than 50% of old data integration solutions based on Data Warehouses fail; and the bigger the spread of data across various systems and sources, the harder companies fail.


As a result, most of the data is stored in data lakes and blob stores, and then a subset of this is stored in the data warehouses.


blob is a data type that can store binary data. This is different than most other data types used in databases, such as integers, floating point numbers, characters, and strings, which store letters and numbers. Since blobs can store binary data, they can be used to store images or other multimedia files.


Enterprises extract data and then load them into data warehouses for descriptive analytics. They became extremely popular 20 years back but they were not built to work with modern data use cases. Then came the emergence of Data Lakes.


Data Warehouses was the response to the “data silo crisis” that showed up in the1990s. Then came the emergence of Big Data, Machine Learning, NoSQL, and ultimately Data Lakes.


Data Lakes improved Data Warehouse architecture by adding the ability to store and analyze structured and unstructured data. Data Lakes were made to be faster by design, making them ideal tools for data scientists. But then Data Lakers presented new problems of its own. Security deficiency, lack of proper data governance, and Data Swamps made business users start to explore other possibilities.



The idea of thinking of data as an ever-flowing river of events instead of thinking of it as a data island locked away in databases coupled with the rapid rise of real-time data activities birthed what is now known as the “Lakehouse paradigm: One platform for data warehousing and data science”.


According to Databricks, A data lakehouse is a new data management paradigm that combines the capabilities of data lakes and data warehouses, enabling BI and ML on all data. This is great! But the challenge is to implement Databricks you need an Army of Data Engineers.

Here’s the thing – According to Marc Andereessen,

“Software is eating the world.” Now 10 years later, all modern companies run on software — exploding IT workloads in the process because only a tiny percentage of people can actually write code.

With these developer shortages and the pandemic accelerating the urgency for every organization to create new digital applications and workflows in hours or days — not weeks or months — we’re witnessing a historic shift in how work gets done.

Cloud is another thing that is inevitable. Eventually, everybody will be in the cloud.

And who says problems says solution!


At AI Surge we believe not just in the technology underpinning the Data Lakehouse paradigm but we want to help businesses adopt the methodology and build in-house Citizen Data Scientists. Accelerating the future with the right mindset will mean that you can innovate as fast as data is being generated.

AI Surge’s No-Code Data Lakehouse platform goes beyond the Databricks lakehouse setup and adds a lot of added features like our “Data DOJO” your personal assistant that does all the data-heavy liting for you,

ability to connect with a variety of sources, perform lightning speed analysis, first of its kind data cataloging system, and data governance.

AI-Surge no-code data lakehouse is the Citizen Data Scientist’s new superpower?”

You must have heard the term “Data is the new oil”, but how do we mine, refine and get the best value from the most important asset to business growth and continuity? In search of answers, Data management is evolving. Today, Data Lakehouse is trending.


Let us go back to the basics of how we handled data– Data Warehousing.


Data warehousing was built basically for Business Intelligence and reporting but with no support for varied data types like video, and audio. No support for Data Science, Machine Learning, and limited support for real-time streaming.


In Gartner’s study, more than 50% of old data integration solutions based on Data Warehouses fail; and the bigger the spread of data across various systems and sources, the harder companies fail.


As a result, most of the data is stored in data lakes and blob stores, and then a subset of this is stored in the data warehouses.


blob is a data type that can store binary data. This is different than most other data types used in databases, such as integers, floating point numbers, characters, and strings, which store letters and numbers. Since blobs can store binary data, they can be used to store images or other multimedia files.


Enterprises extract data and then load them into data warehouses for descriptive analytics. They became extremely popular 20 years back but they were not built to work with modern data use cases. Then came the emergence of Data Lakes.


Data Warehouses was the response to the “data silo crisis” that showed up in the1990s. Then came the emergence of Big Data, Machine Learning, NoSQL, and ultimately Data Lakes.


Data Lakes improved Data Warehouse architecture by adding the ability to store and analyze structured and unstructured data. Data Lakes were made to be faster by design, making them ideal tools for data scientists. But then Data Lakers presented new problems of its own. Security deficiency, lack of proper data governance, and Data Swamps made business users start to explore other possibilities.



The idea of thinking of data as an ever-flowing river of events instead of thinking of it as a data island locked away in databases coupled with the rapid rise of real-time data activities birthed what is now known as the “Lakehouse paradigm: One platform for data warehousing and data science”.


According to Databricks, A data lakehouse is a new data management paradigm that combines the capabilities of data lakes and data warehouses, enabling BI and ML on all data. This is great! But the challenge is to implement Databricks you need an Army of Data Engineers.

Here’s the thing – According to Marc Andereessen,

“Software is eating the world.” Now 10 years later, all modern companies run on software — exploding IT workloads in the process because only a tiny percentage of people can actually write code.

With these developer shortages and the pandemic accelerating the urgency for every organization to create new digital applications and workflows in hours or days — not weeks or months — we’re witnessing a historic shift in how work gets done.

Cloud is another thing that is inevitable. Eventually, everybody will be in the cloud.

And who says problems says solution!


At AI Surge we believe not just in the technology underpinning the Data Lakehouse paradigm but we want to help businesses adopt the methodology and build in-house Citizen Data Scientists. Accelerating the future with the right mindset will mean that you can innovate as fast as data is being generated.

AI Surge’s No-Code Data Lakehouse platform goes beyond the Databricks lakehouse setup and adds a lot of added features like our “Data DOJO” your personal assistant that does all the data-heavy liting for you,

ability to connect with a variety of sources, perform lightning speed analysis, first of its kind data cataloging system, and data governance.

AI-Surge no-code data lakehouse is the Citizen Data Scientist’s new superpower?”

You must have heard the term “Data is the new oil”, but how do we mine, refine and get the best value from the most important asset to business growth and continuity? In search of answers, Data management is evolving. Today, Data Lakehouse is trending.


Let us go back to the basics of how we handled data– Data Warehousing.


Data warehousing was built basically for Business Intelligence and reporting but with no support for varied data types like video, and audio. No support for Data Science, Machine Learning, and limited support for real-time streaming.


In Gartner’s study, more than 50% of old data integration solutions based on Data Warehouses fail; and the bigger the spread of data across various systems and sources, the harder companies fail.


As a result, most of the data is stored in data lakes and blob stores, and then a subset of this is stored in the data warehouses.


blob is a data type that can store binary data. This is different than most other data types used in databases, such as integers, floating point numbers, characters, and strings, which store letters and numbers. Since blobs can store binary data, they can be used to store images or other multimedia files.


Enterprises extract data and then load them into data warehouses for descriptive analytics. They became extremely popular 20 years back but they were not built to work with modern data use cases. Then came the emergence of Data Lakes.


Data Warehouses was the response to the “data silo crisis” that showed up in the1990s. Then came the emergence of Big Data, Machine Learning, NoSQL, and ultimately Data Lakes.


Data Lakes improved Data Warehouse architecture by adding the ability to store and analyze structured and unstructured data. Data Lakes were made to be faster by design, making them ideal tools for data scientists. But then Data Lakers presented new problems of its own. Security deficiency, lack of proper data governance, and Data Swamps made business users start to explore other possibilities.



The idea of thinking of data as an ever-flowing river of events instead of thinking of it as a data island locked away in databases coupled with the rapid rise of real-time data activities birthed what is now known as the “Lakehouse paradigm: One platform for data warehousing and data science”.


According to Databricks, A data lakehouse is a new data management paradigm that combines the capabilities of data lakes and data warehouses, enabling BI and ML on all data. This is great! But the challenge is to implement Databricks you need an Army of Data Engineers.

Here’s the thing – According to Marc Andereessen,

“Software is eating the world.” Now 10 years later, all modern companies run on software — exploding IT workloads in the process because only a tiny percentage of people can actually write code.

With these developer shortages and the pandemic accelerating the urgency for every organization to create new digital applications and workflows in hours or days — not weeks or months — we’re witnessing a historic shift in how work gets done.

Cloud is another thing that is inevitable. Eventually, everybody will be in the cloud.

And who says problems says solution!


At AI Surge we believe not just in the technology underpinning the Data Lakehouse paradigm but we want to help businesses adopt the methodology and build in-house Citizen Data Scientists. Accelerating the future with the right mindset will mean that you can innovate as fast as data is being generated.

AI Surge’s No-Code Data Lakehouse platform goes beyond the Databricks lakehouse setup and adds a lot of added features like our “Data DOJO” your personal assistant that does all the data-heavy liting for you,

ability to connect with a variety of sources, perform lightning speed analysis, first of its kind data cataloging system, and data governance.

AI-Surge no-code data lakehouse is the Citizen Data Scientist’s new superpower?”