The main difference between data lake and data warehouse is that the a data lake can be composed of all types of data varying from structured (relational data), semi-structured (csv, Json, xml among other files) or unstructured (images, videos, text files among any other type of known or unknow files). Some of these data may be known or not. On the other hand, data warehouse, if well planned, are consisted of structured relational data where all the data points are known by the developers and/or businesses that uses its data.
Big data’s value cannot be overstated because it enables organizations to make data-driven decisions, quickly react to changing market conditions, and develop new products that meet customers’ expectations. Machine learning, natural language processing, deep learning, statistics, IoT, and other analytics approaches all rely on big data. However, how do you store vast amounts of information?
When we talk about big data, we are talking about a lot of different forms of structured and unstructured data. Humans generated 1.145 trillion gigabytes of data per day in 2021. It’s because this number is unintelligible that you are having trouble understanding it. This is where Data Lake and Data Warehouse technologies come into play.
Data Warehouses and Data Lakes are used by businesses to store, manage, and analyze data. Data warehouses have a long history as a business technology for storing structured data, cleaning it up and organizing it for specific business needs, and serving it to reporting and business intelligence applications.
What is a Data Lake?
A Data Lake is a centralized area where massive amounts of raw data are stored. Some of this data is not even known by the business. That is the main reason it goes to the lake, because of the lack of knowledge of it or how to use them. However, the main purpose of it is to eventually be able to analyze some of the data and with the help of data engineers and data analytics, filter down some of those data into a data warehouse.
What is a Data Warehouse?
A Data Warehouse allows you to store organized and structured data from a variety of sources, such as marketing, customer connections, and sales. A Data Warehouse consolidates both historical and current data, allowing decision-makers to extract important insights for business intelligence tasks. The primary goal of an enterprise Data Warehouse is to find connections between data from various sources. These various sources usually come from the data lake.
To differentiate them we may think of Data Lake as a huge pool of data with object storage and a flat design, unlike Data Warehouses, where data formats are defined and information is organized and moved to separate related folders.
Data Warehouse VS Data Lake
Types of data
Structured organizational data, such as financial transactions, CRM, and ERP data, is stored in data warehouses. Other data sources, such as social media, web server logs, and sensor data, as well as documents and rich media, are not stored in data warehouses, at first, since they are more difficult to understand and manage due to their vast number. These are the categories of data that are better suited for a data lake up until the business start researching on data that may help somehow the decisions to be taken.
Before data is written and stored, it is organized, defined, and metadata is applied in a data warehouse. This method is known as ‘framework on write.’
A data lake consumes all data kinds, including those that aren’t suitable for a data warehouse. Data is saved in its raw state; information is saved to the schema as data is taken from the data source rather than as it is written to storage. This is known as a “framework on read.”
Data Preservation and Security
Data Analysts spend a lot of time analyzing data and figuring out how to use it for business analysis before it can be stored into a data warehouse.
Data Engineers find ways to pipeline the recent analyzed data into the data warehouse.
They create transformations to summarize and change data so that useful ideas can be extracted. To save storage space and enhance efficiency, data that doesn’t answer specific business queries is excluded from the data warehouse. A traditional data warehouse is an expensive and precious enterprise resource.
Data Scientists look for even more insights of these data. These scientists could be working with data inside the lake as well as data inside the warehouse.
Data Retention is simpler in a Data Lake because it saves all data – raw, organized, and unstructured. Data is never erased, allowing for study of past, present, and future data. Data lakes are simple to develop and grow up to Petabytes in size. They run on low-cost storage devices and run on commodity servers, easing storage constraints.
Protection, development, and application
Data Warehouses are a safe, enterprise-ready technology that has been around for few decades. Data Lakes are on their way, but they’re newer and have a shorter track record in the industry.
A large company can’t just buy and install a data lake like it can a data warehouse; it has to think about which tools to utilize, whether open source or commercial, and how to put them together to fulfil its needs.
The end users of each technology differ: business analysts query the data via pre-integrated reporting and BI in a data warehouse. Because data requires processing and analysis to be meaningful, business users will find it difficult to use a data lake. Insights can be extracted from enormous volumes of data in the data lake by data scientists, data engineers, or savvy business users.
Historical data is stored in Data Warehouses. The structure of incoming data is predetermined. Data warehouses, on the other hand, are insufficient if business questions change or if the company needs to save all data for in-depth research. A Data Lake saves data in its natural state, making it instantly available for study. Data can be retrieved and reused by applying a structured schema to it, storing it, and sharing it with others. If the data is no longer needed, the copy can be deleted without compromising the data in the data lake. All of this is accomplished with no effort on the part of the developer.
Can a data warehouse be inside of a data lake? The answer is yes, however, it would not be considered a well thought design for data storage. Things to take into consideration is security and durability of the data.
Can a data lake live inside of the data warehouse? The answer is no. Remember data lake can also have unstructured data, on the other side, data warehouse cannot.
Which one has more data? The answer should be lakes. The data flow should go from lakes to warehouses. All the data will live in the lake first and then go to the warehouse. There are situations where the data does not need to be in the lake before it goes to warehouse.
As companies transfer their data architecture to the cloud, the decision between a Data Warehouse and a Data Lake, as well as the necessity for complicated connections between the two, becomes less important. It is becoming more common for businesses to have both and to migrate data from lakes to warehouses in order to do business analysis.
In the future years, the amount of data will continue to increase and develop, increasing the demand on cloud storage providers. The inevitable rise in data volumes, which includes operational, streaming, observational, and other data, puts big data storage capacity at risk. Traditional data storage systems are unable to handle terabytes or even petabytes of data, while cloud storage solutions provide the necessary scalability.
Cloud Data Lakes and Data Warehouses can help enterprises overcome capacity constraints and accomplish real-time data goals. Companies can access important data in real-time and make smart data-driven decisions by leveraging the newly acquired scalability and flexible pricing structure.