But what is the safest place to store and integrate data from multiple sources and make the most of it? Both data lakes and data warehouses are popular ways to manage vast amounts of big data. The differences between them lie in how organizations ingest, store and use the data. Read on to know more.
What is a Data Lake?
A data lake refers to a central storage repository where data ingested from multiple sources – in any format (structured or unstructured) – is stored as received. It is like a pool of raw data, the purpose of which is unknown yet. Businesses usually store data that might be potentially useful for future analysis in a data lake. Key features of a data lake:
It contains a mix of useful and non-useful data and hence needs a lot of storage space. Stores both real-time and batch data – for example, you can store real-time data from IoT devices, social media, or cloud applications and batch data from databases or data files. Has a flat architecture. As the data is not processed until it is needed for analysis, it needs to be governed and maintained well; otherwise, it can turn into data swamps.
So, how can we retrieve data quickly from such a vast and seemingly messy storage repository? Well, a data lake uses metadata tags and identifiers for this purpose!
What is a Data Warehouse?
A more organized and structured repository – a data warehouse contains data that is ready for analysis. Structured, semi-structured, or unstructured data from multiple sources are ingested, integrated, cleaned, sorted, transformed, and made fit for use. The Data warehouse contains large amounts of past and current data. Usually, data is processed for a specific business problem (analysis). Such information is queried by Business Intelligence (BI) systems for analysis, reporting, and insights. Data warehouses typically consist of the following:
A database (SQL or NoSQL) to store and manage data Data transformation and analysis tools to prepare data BI tools for data mining, statistical analysis, reporting, and visualization
As data warehouses serve a specific purpose, you’ll always have relevant data. You can also use additional tools in data warehouses to cater to advanced capabilities like Artificial Intelligence and spatial or graph features. Data warehouses created for a specific domain are called data marts.
Key differences between Data Lakes and Data Warehouses
To re-iterate what we read above, the data lake contains raw data whose purpose has not been defined. In contrast, a data warehouse contains data that is ready for analysis and is already in its best form. Some differences between a data lake and a data warehouse are:
Use Cases for Data Lake and Data Warehouse
It is easy to think of a data lake as a more convenient choice because it is more scalable, flexible, and pocket friendly. However, a data warehouse might be a great idea when you need more relevant and structured data for specific analysis. Some use cases for data lake are as below:
#1. Supply chain and management
The tremendous amount of big data in data lakes help predictive analytics for transportation and logistics. Using historic and current data, businesses can plan their daily operations smoothly, inspect inventory movement in real-time, and optimize costs.
#2. Healthcare
The data lake has all the past and current information of patients. This is helpful in research, finding patterns, providing better and ahead-of-time treatment for diseases, automating diagnostics, and getting the most updated details of a patient’s health.
#3. Streaming data and IoT
Data lakes can continuously receive streaming data submitted to analytics pipelines for continuous reporting and detecting any unusual activities and movements. This is possible due to the data lake’s ability to collect (near) real-time data. Some use cases for the data warehouse are:
#1. Finance
A company’s financial information may be more suited for a data warehouse. Employees can easily access organized and structured information in the form of charts and reports to manage the finance processes, handle risks, and make strategic decisions.
#2. Marketing and customer segmentation
Data warehouse creates a single source of ‘truth’ or correct data about customers collected from multiple sources. Companies can analyze this data to understand customer behaviors, offer customized discounts, segment customers based on their preferences, and generate more leads.
#3. Company dashboards and reports
Many businesses use CRM and ERP data warehouses to pull data about external and internal customers. The data is always relevant and can be trusted for creating any type of report and visualization.
#4. Migrating data from legacy systems
Using the ETL capabilities of data warehouses, companies can easily transform legacy system data into a more usable format that new systems can analyze. This will help organizations gain insights into historical trends and make accurate business decisions.
Examples of Data Lake tools
Some top data lake providers are:
Examples of Data Warehouse tools
Some of the top data warehouse solution providers are:
Final Words
Both data lakes and data warehouses have their own benefits and ideal use cases. While data lakes are more scalable and flexible, data warehouses always have reliable and structured information. Data lake implementation is relatively new, whereas data warehouse is an established concept used by many organizations for efficiently managing their internal and external data.
