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
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