Future of cloud-scale analytics is promising on Data Lakehouse
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 it for taking 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✓ Higher costs in hiring people to manage outdated systems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 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, performance, and security
✓ 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
Developing a modern data platform through a combination of technologies helps enterprises make the most of their data by analyzing it for business decision making. 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.
Director | Data & AI | 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.