Your business is as effective as the data you store, manage and analyse. Now, if we keep in mind that data is one of the most valuable assets in an organisation, it stands to reason that it needs to be managed and protected in the best way.
Storage of data though represents a challenge, and with recent studies showing statistics that companies are expected to generate 43 exabytes of data daily, selecting the right data storage solution is tricky to say the least, while vital for competitiveness.
A strong data infrastructure is key, as it fuels innovations and streamlines operations while enhancing decision-making. Therefore, it is vital to understand the differences between a data lake and a data warehouse to prime your data architecture for success, while aligning your data strategy with your business goals.
What is a Data Lake?
If you run a company which generates vast amounts of raw, unstructured and/or semi-structured data, then the data lake storage solution is designed for you.
Unlike traditional storages were data must be pre-processed, data lakes allow all kinds of data, from text, to images, to videos and logs, in their native format.
This flexibility is ideal for companies that need to store different forms of data for future analysis without committing themselves to a specific structure upfront.
What is a Data Warehouse?
On the flipside of the coin, you guessed it, data warehouse requires organised and processed data, which is often segmented in tables and columns, ready to be analysed.
This is because data warehouse is a highly structured data storage solution which typically use predefined schemas, making them the perfect option for operational reporting, business intelligence or BI, and structured querying.
Therefore, while data lakes are flexible, data warehouses excel in managing large amounts of structured data for fast and efficient queries.
Data Lakes & Data Warehouses – Key Differences
As we saw they can both be applied for data storage and both play a vital part in data management, however they hold a different approach to storing, processing, and delivering insights from data held.
Thus, it`s key to understand their distinct functionalities and purposes. Hereunder we list and highlight the core differences to assist you in deciding which solution better aligns with your business needs:
1. Purpose & Utility
Data Lakes – raw, unprocessed data can be explored at a later stage for analytic solutions or machine learning purposes. Therefore, a data lake can be an option when you need to store raw data, with a plan in mind to process it later based on your business needs, like for example extracting relevant insights. This may include storing IoT data, machine logs, and multimedia files.
Data Warehouses – Vast amounts of structured data can be stored in data warehouses, primarily utilised for business intelligence and reporting purposes, yielding clean structured data optimised for analysis. Typical examples are data reliability reporting and historical trend analysis.
2. Data Structure & Schema
Structured vs Unstructured Data – Data lakes are highly versatile as they support both structured and unstructured data. While data warehouses are built around a structured data which follows a consistent schema, making them ideal for operational databases and dashboards.
Schema-on-read vs schema-on-write – data lakes apply schema-on-read meaning that structure is applied only when data is accessed. On the other hand, data warehouses hold schema-on-write which means that the structure is imposed when the data is ingested.
3. Accessibility & Agility
Data Lakes – For data scientists and analysts looking to experiment with different data types, data lakes are the one to go for. For non-technical users though to extract meaningful insights these might be complex.
Data Warehouses – These are easier for non-technical users to utilise SQL queries or BI tools as it is all designed around accessibility, where data is pre-processed and optimised for rapid access.
4. Expenses & Resource Requirements
Data Lakes – In this case the expenses rise during processing and extraction of insights, with data lakes being generally more cost-effective to store large volumes of data.
Data Warehouses – Holding computational resources required to structure and manage data, data warehouses are typically more expensive, however they provide faster performance for query-based workloads.
5. Data Governance & Security
Data Lakes – The unstructured nature of data in data lakes makes it more challenging to implement data governance and security. To track and manage data access proper governance tools are required.
Data Warehouses – These allow organisations to apply access controls and monitor data usage more efficiently, thus they are considered as easier to govern also due to the structured nature.
Real-World Examples of Data Lakes and Data Warehouses
Uber – facilitates millions of rides daily and globally generating large volumes of structured and unstructured data, including rides, payments, user profiles, etc. Uber utilise a data lake structure on Google Cloud Platform (GCP) to effectively tackle and analyse this vast data.
Airbnb – utilizes a data warehouse for business intelligence to optimise prices and user experiences. The warehous holds clean structured data which Airbnb`s analysts can quickly query to attain occupancy rates, seasonal trends, and customer preferences.
Behold Data Lakehouse – The Best of Both Worlds?
I imagine that from the title you already guessed it. Data Lakehouse is the combination of data lakes and warehouses benefits enabling structured and non-structured data storage in one unified platform. It retains the flexibility of a data lake while allowing for schema-on-write like a data warehouse.
Data lakehouse then would be appealing to those organisations requiring big data storage for unstructured data while also needing efficient querying capabilities.
The lakehouse model was pioneered by Databricks, providing companies with real-time analytics and machine-learning skills on the same platform. The data lakehouse option then eliminates the need for data duplication between warehouses and lakes, while optimising expenses and performance.
In Conclusion
For those businesses that are seeking to implement effective data solutions then, understanding the differences between data lake and data warehouse is paramount. Data lakes forte is handling unstructured data for advanced analytics, while data warehouses provide optimised querying and performance for structured reporting.
The emerging data lakehouse model bridges these two worlds, providing a flexible, cost-efficient answer to manage different data requirements. While choosing between these solutions, organisations must carefully consider their data management needs, core users, and performance requirements.