People have always gathered data. Merely, to make use of it later. And, this was never a wrong choice. Humanity used data for all purposes: from understanding the world’s tendencies and trends to being able to make predictions. Today, the amount of data we create, capture, copy, and consume is about 97 zettabytes. Volumes and volumes of such data have to be stored somewhere to be preserved. By 2025, Statista predicts this number to double and increase to 187 zettabytes. And, it asks for bigger storage and means of data preservation.
There is data warehouse vs data lake storage. But there are lots of questions that might sparkle your interest. For instance,
- How can you simply store data there?
- When is it possible to speak about data lake vs data warehouse?
- What is a data lake vs data warehouse?
- What is the difference between data warehouse and data lake?
Eager to know more? Then, make sure to read further!
Data Warehouse. Advantages and Disadvantages of Storage
Let’s start with the data warehouse.
It is a data management system that supports visualization, reporting, and business intelligence. A data warehouse is meant to perform queries and analyses, so it contains large amounts of historical data. The data stored in the data warehouse is obtained from transaction apps and application log files.
A data source can be almost anything: CRM, ERP, legacy, external, and others.
A data warehouse tends to centralize and strengthen large volumes of data from multiple sources. Due to the vast capabilities of data, organizations use data warehouses to get business insights, which later on improve their decision-making, business predictions, business planning, etc.
A data warehouse usually includes
- A relational database (SQL)
- Extract, Transform and Load (ELT) solution
- Analysis, reporting, data mining
- Tools for data visualization
Having a data warehouse creates business opportunities for
- Stability
- Consistency
- Timely change analysis
Working together with such technologies as machine learning and artificial intelligence only allows for advances in the use of data for any business. And this advancement may result in the elimination of manual tasks as well as simplification of setup and development processes.
Advantages and Disadvantages of Data Warehousing
The advantages and disadvantages of data warehouse depend much on the business itself. Particularly, on the use case for data warehouse.
Among the most important benefits of data warehouse are:
- Possibility of historical insights
- Data quality and conformity enhancement
- Efficiency boost
- Data analytics power and speed increase
- Revenue has a tendency to increase significantly
- Scalability is on a high-level
- On-premise and cloud interoperability
- Data security boost
- Query and insight performance is higher
- Major competitive advantage
But, pros and cons of data warehousing also consist of the “cons”. These are
- Inability to capture the data required
- Cost-benefit ratio
- Data censorship
- Flexibility of data
- Processing time of ETL
- Other hidden problems
These points make a real disadvantage of data warehouse. But, let’s remember that these disadvantages work only if your business does not need a data warehouse. What if it needs a data lake and data lake concepts?
Data Lake. Pros and Cons of Storage
What’s a data lake?
A data lake is a repository of data, which is centralized. It allows for the storage of both structured and unstructured data. You can store raw data, run analytical processes in real-time, and so on.
The data types stored in the data lake can be structured, unstructured, semi-structured, and binary. You can use a data lake to filter and process data, for machine learning purposes, data warehousing, visualizations, etc. But, mostly, the data residing in the data lake is unstructured and rather chaotic. It has to be dealt with additionally to pull out the transformed data for any purpose.
According to AWS, businesses that decided to implement a data lake outperformed similar companies by 9%. This revenue growth was quite organic. What exactly did they do? Why data lake? Apparently, they performed machine learning analytics on log files, clickstream data, social media, and devices connected by the internet connection. All the possible data is stored in the data lake. Doing so made it easy to identify extra business growth opportunities, attract customers, boost productivity, maintain devices, and conduct better decision-making.
A data lake shouldn’t be mistaken for a data lake platform. It is rather a container for different varied data that coexist in one great data pool. Can you name a data lake example?
Data lake belongs to a greater enterprise ecosystem, where it is just a small part including:
- Source systems
- Ingestion pipelines
- Integration and data processing technologies
- Databases
- Metadata
- Analytics engines
- Data access layers
That’s what distinguishes data lake vs warehouse. Are there any pros and cons of the data lake to know about?
Pros and Cons of Data Lake Storage
The data lake benefits include the possibility to:
- Democratize data
- Get better quality data
- Support all data storage formats
- Have schema flexibility
- Promote agility
- Receive an advanced analytics
- Get scalability
- Centralize data
- Govern data
- Obtain user productivity
But, despite being at a bigger advantage, there are also cons to consider. Read them carefully as they might be of great concern for your business.
- Storage costs
- Time-consuming
- Limited source data
- Big Data challenge
- Complicated changeover
- Potential for data distortion
So, you had the advantage to learn what is a data warehouse and a data lake. Now, it’s time to compare data warehouse and data lake. Are they really that different? Let’s see.
Differences Between Data Warehouse vs. Data Lake
It is only logical that after the explanations of data warehouse and data lake you’d probably ask what is the difference between data warehouse versus data lake? And the table below will give you the best answers! Data Warehouse vs Data Lake have the following distinctions:
To sum up the table and compare data warehouse and data lake, there are the following differences:
- Data that is being stored: data warehouse has a relational one and data lake has the non-relational
- Schema: data warehouse is schema-on-write, while data lake is schema-on-read only
- Price and performance: data warehouse is faster and costlier and data lake costs less and attempts to work fast
- Quality of data: data warehouse has refined data stored, while data lake has a raw one
- Users: data warehousing is solely for business analysts and data lakes can assist data scientists, data developers, and data analysts if the data is refined
- Analytics to perform: data warehouse is used for visualizations, BI, and reporting, while data lake is for ML, predictive analytics, data discovery, profiling
Another, yet important, comparison of data repositories is via Microsoft Azure. What’s the difference between Azure Data Lake vs Data Warehouse?
These are the most prominent examples of data lake database vs data warehouse differences. But these differences mark the unique data warehouse vs data lake benefits in the appropriate domain. Where can these storage places be used at?
Data Warehouse and Data Lake: Industry Examples of Use
Looking for Dedicated Team?
Did you know that data warehouse and data lake usage can be industry-specific? For example, in healthcare, education, logistics, fintech, etc. you should choose the storage that will best suit the industry’s purpose. Let’s start from the data lake.
Data lake use cases
Any data lake working for a company would be called an operational data lake. Mainly, because the data placed can reveal many operational insights to a business.
- Healthcare. Organizations belonging to the medical sphere can store large volumes of data (structured, semi-structured, unstructured). And they can do it in real-time. Storing this data into a data lake from anywhere possible: from IoT sensors, website’s clickstream activity, log files, different feeds of social media, videos and online transaction processing, etc.
- Education. Educational organizations also receive lots of student data: from registration details to learning matters. The apps containing data are SIS (student information systems), LMS (learning management systems), and Analytics. A data lake can make educational organizations scale their storage capacity when their data volumes grow. This is called a data-driven approach to problem-solving.
- Transportation. Both transportation and logistics businesses can create a single place of storage for multiple source data. The architecture of the data lake is managed, paid per specific use, and quite scalable. Using it can significantly decrease the budget for managing and synchronizing on-premises databases.
Data warehouse use cases
- Banking and finance. With the help of the data warehouse your data can be locked and secured, but there is a possibility to share it with the ones requiring it for their reporting purposes. Banks like implementing a big data warehouse because it can create a copy of all the existing financial data. This copy is available to any banking professional for analysis. The original data is safe and nobody can operate with it. It is better to have structured data in the warehouse than to reposit a finance data lake.
- Public sector. Data warehousing is being used across many fields in the public sector. I.e. retail, insurance, finance, sales, services, health care, education, etc.
- Hospitality industry. The industry includes a large percentage of hotel and restaurant services, car rental services, and holiday home services. So, these services tend to use warehouse services mainly for advertisement design and evaluation purposes. They use feedback and travel patterns to target customers and promote their services to the right audience.
What to Choose for Your Business: Data Warehouse or Data Lake?
Data warehouses are a good solution for small and medium businesses. But, data lakes are rather good for enterprises. However, your data type, and the sources you get it from, count a lot. So, ask yourself the following questions:
1. What’s your data structure? Is it set up?
There are two basic structures to consider — structured (SQL) and non-structured (NoSQL). If you use the first one, a CRM, an ERP, and HRM systems, then consider a data warehouse. But, what if you need a solution that is not based on something that you are using currently? It has to be customized for your business and from scratch. Then, ask yourself the following questions.
2. Is your data unified? How?
If your data is structured, you understand that a data warehouse is the best choice. But, unstructured different source data (i.e. IoT logs, binary data, analytics) would require a data lake. Here you will be able to extract, transform, and load (ETL) data with flying colors. No other option is as good.
3. Do you have to deal with data retention?
Business retention requires gathering historical data and storing huge volumes of this data in a structured database can be quite costly. To decrease costs you can always limit the data being stored. This may also limit the specifics of analysis later.
4. Can you predict your future business needs?
A data warehouse will definitely suit if you need to run a number of queries against the tables that are updated constantly. But if the data is raw, has to be stored as it is and you are up for experimentation in ML, IoT, and predictive analytics — choose a data lake for that mission.
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Originally published at https://inoxoft.com on April 8, 2022.
