Today’s businesses are data-centric. Companies are finding ways to efficiently mine and analyze data from various sources and improve business revenues and profits.
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
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:
Raw or processed data in any format is ingested from multiple sources
Data is obtained from multiple sources for analysis and reporting. It is structured
Schema is created on the fly as required (schema-on-read)
Predefined schema while writing to the warehouse (Schema-on-write)
New data can be added easily
Data is ready after processing, so any new change requires more time and effort.
Data needs to be updated and governed to be relevant
Data is already in its best form, so it does not require specific maintenance
It consists of huge volumes of big data (petabytes)
Data is usually lesser than that in the data lake (terabytes). Data warehouse can contain operational data of an entire organization, analytical data, or data relevant to a particular domain
Used by data scientists for various purposes like streaming analytics, artificial intelligence, predictive analytics, and many use cases.
Used by business analysts for transaction processing (OLTP), operational analytics (OLAP), reporting, creating visualizations
Data can be stored and archived for an extended period to be analyzed at any time.
Data needs to be frequently purged to accommodate the latest data
Storage is inexpensive.
Storage and processing are expensive and time-consuming, hence should be planned judiciously.
Data scientists can develop new problems and solutions by looking at the data.
The scope of data is limited to a specific business problem.
Data warehouses typically use relational databases because the data needs to be in a particular format.
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.
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:
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:
Microsoft Azure – Azure can store and analyze petabytes of data. Azure facilitates easy debugging and optimization of big data programs.
Google Cloud – Google cloud offers cost-effective ingestion, storage, and analysis of huge volumes of big data of any type. It also integrates with analytics tools like Apache Spark, BigQuery, and other analytics accelerators.
MongoDB Atlas – Atlas data lake is a fully-managed data lake store. It provides cost-effective ways to store large-scale data and can run high-performance queries that use less computing power, thus saving time and cost.
Amazon S3 – AWS cloud provides the necessary tools to build a flexible, secure, and cost-effective data lake. It has an interactive console to manage the data lake users and control access to users.
Examples of Data Warehouse tools
Some of the top data warehouse solution providers are:
SAP – SAP data warehouse lets users semantically access rich data from multiple sources. Businesses can securely share insights and models, accelerate decision making and safely combine external and internal data.
ClicData – ClicData’s smart and integrated data warehouse ensures data integrity, quality, and ease of reporting. ClicData offers both scheduling systems and real-time APIs so you can get updated data at all times.
Amazon Redshift – One of the most widely used data warehouses, Redshift uses SQL to analyze all types of data present in various databases, lakes, or other warehouses. It offers a great balance of cost and performance.
IBM Db2 warehouse – IBM provides in-house, cloud, and integrated data warehousing solutions. It also integrates machine learning and artificial intelligence tools for deeper data analysis and shares a common SQL engine for streamlining queries.
Oracle Cloud Data warehouse – Oracle uses an in-memory database and offers graphical, machine learning, and spatial capabilities to deep dive into data for quicker yet richer data analysis.
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.
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