Data Engineering

Increase Data Agility with Data Lakehouse Architecture

The new paradigm that combines the best features of data lakes and data warehouses 

Data lakes are repositories of raw data that can store any type of data, such as structured, semi-structured, or unstructured. 

Data warehouses are databases that store processed and curated data that are optimized for analytical queries. 

Data lakehouse aims to provide the scalability and flexibility of data lakes with the reliability and performance of data warehouses.

In the recent past, data warehouses were the most popular approach to analytics. Being able to analyze large volumes of data and derive insights from it. As the Retail world evolved into exponential growth of available data, a new approach was required to house “big data” due to volume, variety, velocity and of course, value of the data. Fastest growing dataset was unstructured data, that had previously been difficult to leverage, became one of the new “gold mines” and as such NoSQL and data lakes were developed. 

Being able to harness the analytical potential of free form text, voice, and video - first for storage and then for analysis became imperative. Today, with powerful foundation models and natural language processing capabilities, we can process, tag and understand context and sentiment at high degree of confidence. The value derived from data lakehouse architecture is tremendous and can store all data on one platform – coupled with speed, scalability, flexibility, security and governance.

Typical Data Warehouse to Data Lake to Data Lakehouse Migration

The benefit of developing a data lake for Retail organizations was demonstrable in managing cost for a data warehouse infrastructure and acting almost as “staging” area for the data before pipelining relevant datasets into a data warehouse for further analysis. Amazon facilitated this with the introduction of AWS S3 together with Athena SQL interface and leveraging Glue extraction, transformation and loading tool. While extremely beneficial, data lake approach did not deliver analytical performance that fine-tuned data warehouse on Teradata, Redshift, etc. could deliver - so a better approach was desired. The complexity and volume of data in data lakes could lead to the dreaded “data swamp”.

With a data lakehouse architecture, Retail businesses can easily store any type of data, including structured, semi-structured, and unstructured data, in a raw format. This means that they can capture and retain all data without the need for pre-defined schemas. This flexibility allows organizations to quickly adapt and evolve as new data sources emerged. Retail data assets are growing exponentially in volume as we bring in more “atomical” data into our systems. With the emergence of internet of things (IoT), RFID inventory information, integrated telephony systems, chat, security video etc., it is critical to Retailers to lead the way by bringing in a highly dynamic architecture and refine / re-define new datasets.

Data lakehouse is the best of both worlds and a leading approach to solve the challenges in traditional Retail data warehouse architectures with business analytics – offering remarkable benefits like:

  • Data quality: Data lakehouse ensures that the data stored in the lake are consistent, accurate, and complete by applying schema enforcement, data validation, and governance policies. This improves trustworthiness and usability of Retailer data for analytics.
  • Data accessibility: Data lakehouse enables users to access and query data in the lake using various tools and frameworks, such as SQL, Spark, Python, R, or any other BI tools. This reduces the need for data movement and duplication and allows users to leverage the most suitable tool for their analysis.
  • Data agility: Data lakehouse supports both batch and streaming data ingestion, as well as both historical and real-time data analysis. This enables users to handle diverse and dynamic data sources, and to gain timely and actionable insights from their data.
  • Data scalability: Data lakehouse leverages cloud-native technologies - such as object storage, compute clusters, and server less functions, to store and process large volumes of data at low cost and high performance. This allows users to scale their data infrastructure according to their needs and budget.

Some examples of data lakehouse are:

  • Databricks Delta Lake: A platform that enables Retailers to build reliable and performant data lakes on top of existing storage systems, such as Amazon S3 or Azure Data Lake Storage. Delta Lake provides ACID transactions, schema enforcement, versioning, and indexing capabilities for the data in the lake. Databricks also comes with Unity, its built-in data catalogue, to drive data governance and quality.
  • AWS Lake Formation: A service that simplifies the creation and management of secure data lakes on AWS. Lake Formation automates the tasks of collecting, cataloging, cleaning, transforming, and securing the data in the lake. It also provides a centralized access control mechanism for the data in the lake.
  • Azure Synapse Analytics: A service that combines data warehousing and big data analytics in a single platform. Synapse Analytics allows users to ingest, store, query, and analyze both relational and non-relational data in the same environment. It also integrates with various Azure services, such as Power BI, Azure Machine Learning, or Azure Cognitive Services.
  • Snowflake: Snowflake utilizes its Data Cloud to achieve lakehouse benefits, decoupling storage from compute. Users can store structured, semi-structured, and unstructured data while enjoying the benefits of querying data for analytics use cases.

Data lakehouse architecture alone is not the panacea for all data challenges but it does provide a set of tangible benefits worth exploring:

  • Cost-efficiency: Data lakehouses can help organizations save money on data storage and processing costs. This is because data lakehouses can store all of an organization's data in a single repository, which eliminates the need to maintain multiple data silos.
  • Flexibility: Data lakehouses are very flexible, allowing organizations to store any type of data in any format. This makes it easy for organizations to ingest and store data from a variety of sources, including social media, IoT devices, and cloud applications. Beyond the flexibility of data types, lakehouse architecture can be deployed with technologies on AWS, Azure, and GCP, reducing the need to move cloud platforms.
  • Scalability: Data lakehouses are scalable, so organizations can easily add more data and users as their needs grow. This makes it a good choice for organizations that are dealing with large volumes of data.
  • Performance: Data lakehouses can provide good performance for both BI and ML workloads. This is because data lakehouses use a variety of technologies to optimize data access and processing.
  • Governance: Data lakehouses can be governed using a variety of policies and procedures. This helps to ensure that data is managed securely and compliantly.

Here are some questions you can ask yourself to determine if a data lakehouse is right for your organization:

  • Do you have a variety of data sources that you need to store and manage?
  • Do you need to be able to access your data quickly and easily for BI and ML workloads?
  • Are you concerned about the cost of data storage and processing?
  • Do you need to be able to scale your data management capabilities as your organization grows?
  • Are you concerned about data governance and security?

If you answered yes to some of these questions, then a data lakehouse could be a good fit for your organization. 

We at Kloud9 have specialized in Data Engineering and Cloud technologies for years serving major global corporations with diverse needs. We help you build your roadmap for digital transformation with state-of-the-art services and solutions adding significant value to your enterprise – from ideation to experimentation all the way to productionisation and MLOps. 

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