Senior Vice President of Gartner, Peter Sondergaard, said that information is the fuel of the 21st century, and analytics is the engine. Companies have always run by data, and increased usage of the internet resulted in more data being generated than ever before, which evolved the term, Big Data. With data being created at a gigantic scale, you will need a place to stock up all this data. So, here need for data warehouses and data lakes solutions come in.
Companies have long dependent on BI Analytics to help them shift their businesses ahead in the competition by discovering hidden opportunities from data. A few years ago, converting BI into actionable information needed the assistance of data experts. But today, various technologies support Business Intelligence and analytics that can be used easily by employees of all levels within the organization.
Everything that BI data needs to store it somewhere. The data storage option you choose decides how easily you can access, secure, and use data in different ways. That’s why it is important to understand the basic alternatives, how they’re different, and when you should use them.
Why Data Warehouse and Data Lakes are Important?
Both data warehouses and data lakes are extensively used for storing big data, but they are not similar terms. A data lake is a huge pool of raw data, and a data warehouse is a central repository for structured, clean data that has already been processed for a definite function.
It is common that people often get confused between two types of data storage, but they are much more different than their similarity. In reality, the only likeness between them is their sophisticated intention of data storage. The dissimilarity is important because they provide different functionalities and need diverse sets of skills to be correctly optimized. While a data lake serves good for one company, a data warehouse can be suitable for another.
What is a Data Warehouse?
A data warehouse is a combination of different technologies and components that allows the strategic exploitation of data. It is a practice for gathering and managing data from wide-ranging sources to deliver meaningful business insights. The electronic storage system saves a large volume of data generated by a business, which is intended for query and analysis instead of processing transactions. Data warehouse performs the process of converting data into information.
Modern enterprise data warehouse (EDW) is a database, or assortment of databases, that unifies a business data from numerous sources and applications, and keeps it ready to access for analytics and operation within the organization. Companies can include EDW in an on-premise server or in the cloud.
The data stored in such a digital warehouse is one of the most valuable assets of a business. It showcases much of what is extraordinary about the business, its people, its customers, its stakeholders, and more.
Advantages of Data Warehouse:
- Superior ability to analyze relational data that is flowing through online transaction processing (OLTP) systems and business applications (e.g., ERP, CRM, and HRM systems)
- High-quality integration with consistent data sources, particularly for relational sources, making it robust for small to medium-sized businesses
Disadvantages of Data Warehouse:
- Data silos, in which information security maintenance directs to restricted access such that important data isn’t reached by the people who may have profited from it, obstructing efficiency and collaboration
- Higher prospects of distortion of BI analysis outcomes due to impulsive or wrong data cleansing — since data quality is frequently one-sided, with diverse analysts having various tolerances for what comprises quality
What is Data Lake?
A Data Lake is a repository that can store massive volumes, including structured, semi-structured, and unstructured data. It lets you store every sort of data in its native format with no fixed borders on account size or file. Data Lake supports large data quantity to boost analytic performance and native integration.
Data Lake is similar to a big container, just like real lakes and rivers. As lakes have numerous tributaries flowing in, a data lake contains structured data, unstructured data, logs, machine to machine data streaming in real-time.
Advantages of Data Lake:
- Simple integration with the Internet of Things (IoT), as data like IoT device logs and telemetry, can be gathered and analyzed
- First-class integration with machine learning (ML), with the schema-less structure and ability to amass large volume of data
- The flexibility provided by the schema-less structure that assists in evaluating data coming from social networks and mobile devices. Also, it carries large, varied, multiregional, and micro-services ecosystems
Disadvantages of Flexibility in Data Lake
The flexibility offered by data lakes can lead to mistreatment, making shortcomings that create more problems than they solve. For example, Data graveyards are data lakes storing data that is collected in large amounts but never used, and Data Swamps are data lakes with low-quality data.
Key Difference in Data Lake and Data Warehouse
Based on some key factors, let’s see how the two data storage terms differ from each other:
In data lakes, all data is stored regardless of the source and its structure in its raw form. It is only processed when it is all set to be used.
A data warehouse will comprise data that is pulled out from transactional systems or data that includes quantitative metrics with their traits. Then the data is cleaned and transformed or further process.
2. Data Capturing
A data lake captures every type of data in their original format from real source systems, whether it is structured, semi-structured, or unstructured.
A data warehouse captures structured information and arranges it in various schemas as classified for data warehouse purposes.
3. Data Timeline
Data lakes can store all data, not only the data that is already in use but also data that it can use in the future. Also, data is saved for all instances, to go back in past data and conduct an analysis.
In the process of data warehouse development, considerable time is spent on evaluating different data sources.
Data Lake is perfect for users who like to conduct deep analysis. Such users incorporate data scientists with knowledge of advanced analytical tools to exploit functionalities, such as predictive modeling and statistical analysis.
The data warehouse is suitable for operational users since it is well structured, easy to use, and understand for general employees.
5. Storage Costs
Storing data in big data technologies is comparatively low-priced than storing data in a data warehouse.
In a data warehouse, storing data is expensive and time-consuming.
Data lakes can include every data and data types, as it allows users to access data before the process of transformation, cleaning, and structuring.
Data warehouses can deliver insights into pre-definite questions for predefined data types.
7. Processing Time
Data lakes leverage users to use data before it has been converted, cleansed, and structured. Thus, it enables users to obtain their results more rapidly comparing to the traditional data warehouse system.
Data warehouses provide insights into pre-definite queries for pre-defined data forms. So, any changes in the data warehouse required more time.
8. Position of Schema
Generally, the schema is determined after storing the data in data lakes. It offers more agility and easiness of data capture but needs efforts at the end of the process.
In a warehouse, the schema is determined before storing the data. It needs efforts at the beginning of the process but presents good performance, integration, and security.
9. Data Processing
Data Lakes works based on the process of ELT (Extract, Load, and Transform).
Data warehouse works on the basis of traditional ETL (Extract, Transform, and Load) process.
Data is stored in its raw form in data lakes and transformed only when it is ready for use.
The major complaint against data warehouses is its lack of ability to make changes in them.
11. Key Benefits
They incorporate various types of data to generate entirely new questions as these users may not possibly use data warehouses because they want functionalities beyond their potential.
In a data warehouse, most of the operational users only are concerned about reports and key performance evaluations.
Data Lake vs. Data Warehouse In Different Industries
Sometimes, organizations often require both. The need for Data Lake arrives to connect big data and take advantage of the raw, coarse structured and unstructured data for technologies, such as machine learning, but there is always a need to build data warehouses for analytics for the use of business users.
1. Healthcare: Data Lakes Store Unstructured Information
Data warehouses have been used for a lot of years in the healthcare industry, but it has by no means been immensely successful. Due to the unstructured behavior of most of the data in the health industry, such as physician notes, clinical data, etc. and the requirement for real-time insights, data warehouses are typically not a perfect model.
Data lakes enable you for a mixture of structured and unstructured data that can be a better match for healthcare companies.
2. Education: Data Lakes Present Flexible Solutions
In modern years, the worth of big data in education modification has become extremely apparent. Data about student scores, attendance, and more cannot only help to weaken students revert on track but can truly, help to forecast possible issues before they happen. Flexible big data tools have also assisted educational institutions in modernizing billing, progress fundraising, and many more.
Most of this data is huge and exceedingly raw- so most of the time institutions in the education field, leverage the best benefits from the flexibility of data lakes.
3. Finance: Data Warehouses Pleads to the Masses
In the finance industry and other economic business setups, sometimes a data warehouse serves as the most excellent storage model because they get the ability to access structured data by the whole company not only a data scientist.
Big data has assisted the financial and economics industry take large steps, and data warehouses have been a top performer to help companies take that step. The only cause that can influence financial services company away from such a model is because it is more economical, but not as successful for other functions.
4. Transportation: Data Lakes Help To Make Predictions
A great amount of the profit of data lake insight is its capability to make predictions.
In the transportation business, particularly in supply chain management, the prediction ability that approaches from flexible data in a data lake can have enormous benefits, specifically cost-cutting reimbursements identified by analyzing data from forms within the transport channel.
Which Solution Is Right for Your Business?
When you have collected the whole information, you can conclude which BI data storage solution is ideal for your business efforts — whether you should choose a data lake or a data warehouse? After all, both of the solutions provide good data storage facilities for appropriate use cases. The answer to your question may be one or both based on your specific needs, or the businesses can make use of both solutions at the same time.
In general words, the use of data warehouses can be common for small to medium-sized businesses, while data lake practices are more general for superior enterprises. Deciding one alternative for your business often depends on your data sources. For example:
- If you utilize an SQL database or ERP, CRM, and HRM systems, data warehouses will fulfill your enterprise environment perfectly.
- If your data flows in from different data sources, such as NoSQL, IoT logs and telemetry, mobile, social data, and web analytics, data lakes are possibly a good option.
When you run a business, profit, or loss of your business depends on the decisions you made. So when it comes to making the right choice can be essential to ensure that the tool you choose delivers the optimal value to your business. However, the data you confine can only be valuable if you can transform it into actionable insights. Today, top-most software companies like Informatica, Tableau, IBM, etc. offer data analytics tools that let you make decisions easily concerning your upcoming plans and present actions.
Are you still confused about whether to choose an enterprise data warehouse or data lake solutions? Get expert guidance now!