In the world of data storage, you might hear terms such as data warehouse and data lake, but you might wonder what they are, what the differences are between them, and when to choose them.
In this article, we will look at what data lakes are, including the MongoDB Atlas product Data Lake, how they differ from data warehouses, what the architecture of a data lake might look like, and the benefits of using one.
At its most basic level, a data lake is a store for large amounts of data in its raw, original format ahead of being used in analytics applications. It’s often used as a central storage for all data for an organization, allowing it to be stored as is, ready for analysis by different stakeholders.
The data can come from disparate sources and will often be stored as a vast collection. It can support every type of data, from structured to semi-structured to unstructured, making it flexible for many scenarios. It’s all stored in a flat, non-hierarchical format, as objects with metadata.
Examples of types of files that you may find stored in a data lake include audio files, PDF documents, JSON documents, relational data, and text files.
MongoDB Atlas has a fully managed, high performance data lake solution called Data Lake, optimized for analytical queries while maintaining the benefits of cost-effective cloud object storage.
Atlas Data Lake can be considered a combination of four different pieces of functionality:
These four pieces of functionality provide you with a workload-isolated view of your cluster data in economical object storage that can support large data sets, with high performance for analytic type queries.
Since they are both commonly used in data analysis scenarios, there is sometimes confusion of the differences between a data lake and a data warehouse and why to use one over the other.
|Data Lake||Data Warehouse|
|Structure||Structured, semi-structured, or unstructured||Structured or semi-structured|
|Data||Stored raw until needed||Processed on ingestion|
|Schema||Not required for ingest (schema on read)||Fixed|
|Users||Developers, business analysts, and data scientists||Business analysts and data scientists|
|Scalability||Can scale exponentially at a low cost with any data type||Scaling can get expensive depending on vendor|
|Architecture||Flat||Hierarchical with tables|
For a more detailed comparison, you can read the databases vs data warehouses vs data lakes article.
Data lakes are becoming increasingly popular for businesses as a way to store, analyze, and share large amounts of data. A data lake can provide various advantages over traditional data storage solutions such as databases.
One of the primary benefits of using a data lake is scalability. Data lakes are designed to store large amounts of data and can easily scale to handle increasing demands. This makes it possible for businesses to store more data without the need for additional hardware or infrastructure.
Data lakes also provide more data access and flexibility. Data can be accessed from a variety of sources, including structured and unstructured data, and can quickly and easily query, analyze, and process data. This makes it easier for stakeholders to gather insights and make decisions based on the data.
In addition, data lakes provide a secure solution for storing data. Data stored in a data lake is encrypted and secured, ensuring that data is kept safe and secure. This makes it possible to securely store sensitive data without the need for additional security measures.
Performing long-running or large analytical queries can be very resource intensive for your systems. If these queries are run against live systems with production data, this can have an impact on the performance of the application and cause a poorer experience for end-users. With Atlas Data Lake, you can extract data to an isolated data store, decoupled from the data store for computation. This workload isolation allows for high-performance analytics while not impacting application workloads.
As mentioned earlier in this article, Atlas Data Lake is optimized for analytical queries. One way it does this is using partition indexes. These are a range of statistics about the underlying partitions of data, to help target queries to the relevant files faster and more efficiently, leading to faster queries and minimal data scans.
This is backed up as well by an analytic oriented file format, based on open source standards with improved support for MongoDB data. This allows for fast point-queries and aggregate queries. Aggregate queries only scan the columns required to provide results, leading to higher performance.
Plus, Atlas Data Lake can be configured to use snapshots of your data at scheduled intervals, meaning not only do you have a view of your data as it was at different points in time, but you can also carry out queries against those different snapshots.
Due to it being a large central repository, or “dumping ground” for large streams of data, either in frequency or size, there is no set architecture when creating or maintaining a data lake. However, there are some things to consider that play a part in the architecture.
You might want to consider the types of data that will be stored and the use cases — for example, the types of analysis that will be carried out against the data.
Secondly, consider a storage hierarchy to organize the data such as folders for different data sets, data, or file types.
You may also want to consider some kind of data governance policy to ensure the security and integrity of the data being stored.
Next, consider the data ingestion process and how data will be moved into the data lake. This may include using batch processes for importing large amounts of data, as well as streaming technologies for real-time data ingestion.
Overall, the key to architecting a successful data lake is to design a scalable, flexible, and secure infrastructure that can support the storage and processing of a wide variety of data. This is where a product like MongoDB Atlas Data Lake is ideal.
There are many benefits to using a data lake when you want to store and later analyze data:
In this article, you have learned about what a data lake is, why you might use it, and some suggestions to consider when architecting your data lake. You also learned about how MongoDB Atlas Data Lake is an ideal solution for your data lake as it’s optimized for analytics and high performance.
Data lakes and data warehouses are two different types of data storage solutions. A data lake is a large repository of raw, unstructured data that is stored in its native format. It’s designed to store large amounts of data from multiple sources and is used for data analytics and machine learning. A data warehouse, on the other hand, is a structured repository of data that is designed for data analysis and reporting. Data warehouses are designed to store data from one source and are optimized for analytics and reporting.
A data lake is a large repository of raw, unstructured data that is stored in its native format. It’s designed to store large amounts of data from multiple sources and is used for data analytics and machine learning. Data lakes provide an efficient way to store and access large amounts of data, allowing users to quickly and easily access and analyze data.
Data lakes can provide a range of benefits for businesses and organizations, including: