About Motifworks

At Motifworks, we are AZURESMART. We are one of the fastest-growing cloud solutions providers, specializing in Cloud Adoption, Application Innovation, and Effective Data Strategies. Our passion is to empower you to accelerate your digital transformation initiatives using the Microsoft Azure cloud. We’re here to simplify your path to explore what’s possible.

Corporate Office

200 W Townsontown Blvd, Suite 300D, Towson, MD, 21204, US

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Philadelphia ¦ Detroit ¦ Dallas ¦ Florida ¦ Cincinnati ¦ Ohio

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101 Specialty Business Center, Balewadi Road, Pune- 411045, Maharashtra, India

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info@motifworks.com +1 443-424-2340
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Women showing cloud scale analytics on screen

Future of cloud-scale analytics is promising on Data Lakehouse

Future of cloud-scale analytics is promising on Data Lakehouse

Women showing cloud scale analytics on screen

Number of data sources in an organization constantly keeps on increasing as the company grows and adds more departments and employees. With increasing data within an organization, numerous questions start passing the data practitioners minds about how they can capture this data and start capitalizing on cloud scale analytics to take data-driven business decisions. Data is valuable to all organizations and is beneficial as well when data is translated into insights through accurate data modelling.

There are various options when it comes to storing data in data repositories that can gather, store, manage, and segment data for analysis or reporting. Based on an organization’s requirement or maturity in data analytics practices, legacy Data Warehouse and cloud Data Warehouse has widely been adopted by enterprises to support their decision support systems.

But since almost a decade, the use of legacy/on-premise data warehouses systems has fairly low because businesses realized the investment and on-going costs of maintaining and supporting was too high. Also, they are built on Relational Database Management System (RDBMS) which supports transactional support but lack in performing BI operations. They provide great consistency and reliability to our data as it originated from a transactional system.

Take a look at some of the business benefits and challenges of using a legacy Data Warehouse:

Business Needs of Legacy Data Warehouse and Business Intelligence Environment

Combine data from multiple databases and data sources
Achieve increased power & speed of data analytics
Gain historical intelligence through enhanced data quality

Challenges of Legacy Data Warehouse and Business Intelligence Environment

No support for video, audio, text
No support for data science, AI/ML
Inadequate to meet real-time data
Not very efficient in unstructured data handling
Closed and proprietary formats
Rigid architecture hampers business agility
Higher costs in hiring people to manage outdated systems

Most of the data these days is stored on Data Lakes due to the challenges of legacy Data Warehouse. Data Lakes were developed to consolidate data from all sources of an organization in a single & central location. The concept of Data Lakes was introduced when Big Data and Hadoop ecosystems started getting popular and everything was stored in an HDFS lake. Technologies like Spark were used to query data stored in Data Lake for analytics. Data Lakes can process all types of data – including structured and unstructured data that is today important for business use cases supported by machine learning and advanced analytics. However, Data Lakes offer poor reporting, cloud-scale analytics, and BI support to enterprises and is very complex to setup and needs skilled data engineers to experience its full potential.

Here are some of the benefits and challenges of the Data Lake solution:

Benefits of Data Lake Solution

Ability to store data in any type of format
Storing data is easier without any pre-defined schema unlike data warehouse
Scalable as compared to traditional data warehouse

Challenges of Data Lake Solution

No consistency means impossible to mix data appends and reads
Difficult to handle updates and deletes
No atomicity means failed jobs leave data in corrupt state
Handling of stream and batch data
Historical versions are costly
Difficult to handle meta data
Too many file problems and performance issues
Data quality issues

Enterprises these days are creating an environment where they can get the best of both worlds by setting up both Data Warehouse and Data Lake. Instead of having 2 environments at the same time, they started using a unified solution. This gave birth to the Data Lakehouse architecture which has a warehouse layer over a transactional storage layer on top which offers reliability, scalability, and agility. A Lakehouse provides BI and reposting functionalities to enterprises by merging structured and unstructured data collected from Data Warehouse and Data Lake systems.

The structured transaction layer brings together quality, governance, and performance to the Data Lake which is missing these days. In a Lakehouse paradigm, this structured layer is provided by Delta Lake. This is possible through Delta Lakes which brings the best of Data Warehousing and Data Lakes together and is an open-source technology and open-source systems. Delta Lake is a layer which is built on top of the Data Lake, offers reliability, quality, and performance to Data Lake.

How Delta Lake combines the best of data warehouses and data lakes for improved data reliability and performance to offer cloud scale analytics

Delta Lake is an open-source technology developed by Databricks, and it solves the problems of Data Warehouse and Data Lakes by combining the best of both worlds in one solution
Through ACID transaction, Delta Lake puts a transaction log in data lake’s open parquet files which ensures every transaction fully succeeds or gets cleaned up and aborted
Supports Apache Spark for scalability and handling petabytes of data, and uses single nodes to handle small metadata
With a technique called Data Skipping, it is easier than ever to store and read only a specific data set in case of a query is generated
With a feature such as Z-ordering, index multiple columns at the same time and access those columns quickly
Seamlessly integrate Delta Lake with Power BI on top of data lake to architect a powerful data platform and make data-driven decisions


cloud scale analytics with data lakehouse

Image Source: Microsoft Tech Community

Developing a modern data platform through a combination of technologies helps enterprises make the most of their data to carryout cloud scale analytics. Data Lakehouse architecture infuses the best features of Data Warehouse and Data Lake into a single solution and is a low-cost solution when compared to both the solutions. Depending on what data is important to an organization, Data Lakehouse is an ideal and high-performance data management architecture that gives enterprise data a shape which can be modelled for analysis, BI, and reporting requirements. Motifworks has proven expertise in helping enterprises reimagine their data platforms on Lakehouse architecture. Our Power BI Consulting services covers assessment and replatfoming of your existing data platform on powerful and scalable Data Lakehouse.

Tarun Agarwal, Team Motifworks

Tarun AgarwalTarun Agarwal

VP- Cloud Solutions | Motifworks

Known as a Data Analytics thought leader who fuels data-driven transformations for Fortune 500 firms, Tarun’s passion is to tell the “story” of the data that is hidden in an enterprise’s data assets. He does this flawlessly by leveraging Big Data, Machine Learning, AI, and cloud platforms. Tarun’s expertise lies in modernizing data platforms through cutting-edge technology solutions and at Motifworks, Tarun leads the Data & AI practice.

Not utilizing your unstructured data to its full potential?

Data Lakehouse does it for you to take data-driven decisions and gain BI capabilities