Is data lake a database?
One can often confuse a data lake with a database. After all, both store data. But they’re not the same. A database stores current data, whereas a data lake stores historical and current data.
You require a database management system (DBMS) to operate a database, and several database types exist. Also, a database has a predefined schema and stores structured or semi structured data. For example, a relational database stores data in a predefined tabular format.
A data lake can have data in database tables since it doesn’t require a predefined structure. However, a data lake has better analytical capabilities as it can store several data types.
Key concepts of data lake
Understanding data lakes can be challenging. The following sections explain four concepts to help clarify the data lake platform.
Data sources and integration
You can integrate your data lake with several data sources. Data sources, as mentioned earlier, can include mobile apps, IoT sensor data, the internet, internal business applications, etc. The sources can be in different formats.
Data storage means recording and retaining data in a particular platform. Traditionally, people stored data such as documents, images, and other files on local disks. Today, you can store data on large data servers.
A data server is a piece of computer hardware responsible for storing, managing, and providing user data. A data lake can consist of several data servers which integrate with a company’s data sources.
Also, the servers can be on-premises or on the cloud. A company has complete control and ownership of on-premises servers.
Cloud data storage consists of third-party servers. For example, Amazon provides cloud services called Amazon Web Services (AWS). It owns an extensive server network globally and sells data storage and management services to other businesses. You can purchase AWS services and build a cloud data lake on the AWS platform.
Cloud data lakes provide more scalability and data reliability as the cloud service provider manages the infrastructure. However, services can be expensive, and customizability can be low. Businesses can adjust on-premises infrastructure as required as they get to have complete control.
Data querying and analysis
A data lake aims to help users with big data analysis. But analysis requires data extraction — requesting a data lake to provide you with relevant data. After the platform receives the request, it processes it and provides the desired results.
The most common way to request data is through writing data queries. A query helps you get relevant answers and insights from data. For example, you can ask a data lake, “What were the sales in the last month?”. After processing the query, it will generate a figure indicating last month’s sales.
The only difference is that you must convert the question into language a computer can process. Most data lake platforms support the Structured Query Language (SQL) to process information. You can use SQL to analyze existing data directly by writing queries that perform various calculations.
Or, you can extract and analyze the relevant data through an external tool such as Microsoft Excel, Tableau, PowerBI, etc. Of course, you will need more advanced analytics tools to study unstructured data. For example, Python — a high-level programming language — helps analyze unstructured data efficiently.
Managing large volumes of data is time-consuming and costly. However, data lakes simplify processing data through metadata management. Metadata is data about your data. For example, you can have a sales database in your data lake.
The database can contain several tables concerning sales information. It can have data regarding products, customers, inventory, purchases, etc. A data scientist can require such data to predict sales for the coming year.
Since sales is not a data scientist’s domain, they may need help understanding several columns in a particular table. So they can refer to the table’s metadata to understand each column’s meaning.
The metadata can provide domain-specific knowledge, information about the creators, date of creation, recent updates, data formats, etc. Using metadata, a data scientist can write appropriate queries to select the correct columns for analysis.
Data lakes automate metadata management. For example, they can automatically assign definitions to data according to a business glossary. Of course, you must first establish a standard glossary with all the relevant terms and their meanings. As data lakes ingest data, they can apply the terms appropriately.
How data lakes differ from data warehouse?
A data warehouse and a data lake are different storage platforms. A data warehouse typically stores structured data only. It doesn’t store unstructured data. And this means a data warehouse has a predefined schema.
For example, a data warehouse can contain several databases for specific lines of business. It can have finance, sales, and supply chain databases. Each database will have well-defined tables, and each table will have specified rows and columns.
The rigid structure of a data warehouse makes it suitable for creating standard reports. Business analysts can use it to provide business intelligence through intuitive visualizations. Also, the well-organized formats make data access easier to control.
But identifying relationships and managing access in data lakes requires an expert. That’s because traditional data lakes store data from several sources.
However, data lakes don’t require a structured data format. They apply a particular schema to raw data automatically. Plus, data lakes let you perform Machine Learning (ML) and data science tasks using SQL with low latency. And that’s why data engineers, scientists, and analysts can easily use machine learning data lakes.
Also, implementing changes in data lakes is easier. You don’t have to apply transformations to give structure to your data. Making changes in a data warehouse is time-consuming.
You must transform data, so it agrees with the warehouse’s design constraints.
What can a data lake store?
As mentioned, a traditional data lake can store relational data in table formats. They can also store non-relational data from mobile apps, videos, audio, IoT sensors, social media, and JSON or CSV files.
How is a data lake architected?
Data lakes can have several architectures. Depending on the requirements, a data lake architecture can combine several tools, technologies, services, and optimization layers to deliver data storage and processing.
However, a typical data lake architecture requires a resource management facility. The facility ensures optimal resources are available for storage and processing.
Also, since many users access a data lake, an access management service is necessary. The service must provide the relevant tools to help the data administration team manage access.
In addition, a data lake architecture needs a metadata management layer to profile, catalog, and archive data. The process maintains data integrity and improves searchability.
Plus, it requires Extract, Load, and Transform (ELT) pipelines to help users extract data and load it into analytical platforms. Such pipelines define a set of automatic processes for bringing data into a usable format.
And, of course, it requires analytical tools, allowing users to gain valuable insights from processed data. The tools should have visualization features and support several development environments. They should also work with multiple programming languages for better usability.
Further, the architecture must have robust data lake security protocols to ensure data quality and compliance. For example, they should let administrators encrypt sensitive data quickly. They must also allow for easy monitoring of data movements to detect data corruption.
Lastly, a comprehensive data governance framework must guide all data lake activities. It can consist of built-in checks within the platform or best practices that all teams follow. Such frameworks define how users store, create, share, and access data.
What are the key components of a data lake?
A data lake consists of five critical components based on the abovementioned architecture: data ingestion, data storage, data transformation, data serving, and data exploration.