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A data lake is a centralized repository of all an organization’s data stored in its raw format. This allows enterprises to store all their data, in its natural or raw state, in one location. This includes structured, relational data with rows and columns, semi-structured data such as CSV or XML files, and unstructured data such as emails, documents, or PDFs.
The data lake holds every file of data within the enterprise so that no files will get lost, and any data can be pulled for analysis, visualization, or machine learning projects.
What is the difference between a data warehouse and a data lake?
Data lakes and data warehouses are both used for storing large amounts of data, but they are not the same thing. A data lake is a big pool of raw data that doesn’t have a specific purpose yet. It is just a place to store all data in its natural form for later use. A data warehouse is a repository for structured data that has a specific purpose and has already been processed for that purpose.
So a data warehouse is used to store big data for a specific project, while a data lake is used to store vast amounts of big data that can be pulled and used for any project. Since data lakes hold raw data, the data would then need to be processed for its new use, and then it would go to a data warehouse specific to its purpose.
Why is it called a data lake?
The term data lake was coined by James Dixon. He described the term with an analogy about drinking water. In his analogy, a data warehouse is like a store of bottled water, cleansed and structured for consumption, and the data lake is like a large body of water in its natural state. The data lake has a stream coming in to fill the lake, and users of the lake can come to dive in or take samples.
What is a data lake used for?
Put simply, a data lake is used for storing all of an organization’s raw data. It can be stored on-prem or in the cloud, but the real use of a data lake is the endless possibilities for which the data in the lake can be used. Data lakes have been criticized and called “data graveyards” because if all data is thrown into a data lake but never used, then the value of the data lake is lost.
The key is to take advantage of the possibilities for the data in the data lake. It is not just a secure place to store big data. It’s a place to look for data that can be used or reused for analytics or machine learning models. The scale of the data lake is amazing when you consider all the insights that could potentially be gained with all that data.
The problem is that companies don’t always have the data management resources to keep track of what is in their data lake and what could be used for upcoming data projects. However, if data science teams have access to the data lake and are trained to always look for data to repurpose before spending time on acquiring new data, the value of the data lake can easily be seen.
Why do organizations need data lakes?
A sure way for an organization to outperform their competitors, is to successfully generate business value from their data. In this data-driven era, an astounding amount of data goes to waste due to poor data management. Implementing a data lake is the first step to generating more value from your organization’s data.
Keeping all your organization’s data in a central data lake will allow you to do new types of analytics such as machine learning with any source from anywhere in your organization. This will give you insights that you never had access to before, allowing you to make informed decisions to take advantage of new opportunities for business growth and improved efficiency.
The benefits that big data insights can bring your organization include:
- Attract and retain more customers
- Improved customer experience
- Increase productivity
- Increase operational efficiency
- Proactively maintain equipment and devices
- Improve R&D innovation choices
- Make more informed decisions
These improvements throughout the organization require truly informed decision making. So, if an enterprise is tossing data or managing their data poorly, they won’t be able to reap the benefits of big data insights. A data lake solves broad data management issues and allows a company to improve their organization with insights from analytics and machine learning models.
Using a data lake with machine learning
When a company has all their raw data stored in a data lake, it allows them to pull from any of it to use in their machine learning models. Here at Algorithmia, we’re experts at deploying machine learning models into production quickly, because we know the value that machine learning can bring to an organization. But in order to realize the value of machine learning, you have to have data. This is easy if your organization stores all data in a data lake, so that data scientists and engineers can easily access any data they need.
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