The amount of data is growing at a staggering rate of 19.2 % per year, making it difficult for organisations to keep up. While the sheer volume of data - particularly unstructured information from websites, social media and IoT devices - can be overwhelming, it also presents many opportunities. McKinsey reports that with the right data architecture, companies can realise an annual return on investment of up to $500 million.
One example of such an architecture is the data lake, which enables companies to utilise large volumes of different data while adapting to changing requirements. In this article, we look at data lakes, their architecture and the benefits they can have for your company.
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What is a data lake?
A data lake is a Centralised architecture designed for storing large amounts of data in raw format - unstructured or semi-structured. This is a flexible storage space for all types of Databefore we know exactly how we want to use them. This allows us to store different types of data and adapt to changing analysis requirements over time.
Differences between data lake and data warehouse
Data warehouses and data lakes are both used as data storage solutions, but are geared towards different needs and workflows.
| Feature | Data Lake | Data Warehouse |
|---|---|---|
| Schema approach | Schema-on-Read: Data is stored in raw format and structured as required | Schema-on-write: Data must correspond to a predefined structure before being saved |
| Flexibility | High flexibility, supports various data types and advanced analyses | limited flexibility, optimised for structured data and consistent queries |
| Processing method | ELT: Extract, load, then transform, enables data conversion as required | ETLExtract, transform, then load to ensure that only processed data is saved |
| Use cases | Big data analyses, machine learning | Business IntelligenceReporting |
| Scalability | Easier, faster and more cost-effective to scale | More processing power required, possibly slower to scale |
| Data quality | Raw data, not standardised or quality-checked | Structured, cleansed and standardised data |
Understanding the differences between data lakes and data warehouses is crucial to effectively utilising the enormous volumes of data. We shed light on all the important aspects in our article:
Differences between data lake and data mesh
Data Mesh and data lakes are two different approaches to managing and organising data within a company. As with data lakes, a centralised architecture provides a single storage location to store large volumes of data efficiently and cost-effectively.
In contrast, a data mesh takes a decentralised approach in which ownership and responsibility for the data is distributed among departmental teams. Each area manages its own Data as a product and ensures that they fulfil certain quality and Governance standards fulfil.
While data storage is centralised in a data lake to ensure broad accessibility and scalability, data management is decentralised in a data mesh to adapt it to the organisational areas, which enables flexibility and context-specific control.
| Feature | Data Lake | Data Mesh |
|---|---|---|
| Data ownership | centralised, all data is stored in a repository | Decentralised, domain-oriented teams own data |
| Data structure | Raw, unstructured and semi-structured data | Domain-specific, structured data products |
| Data governance | Centralised governance with a uniform model | Federated governance across domains |
| Scalability | Scalability with storage capacity and data volume | Scalable with domain teams and their requirements |
| Flexibility | high, supports various data types and use cases | high, tailored to sector-specific requirements |
| Data access | Uniform access throughout the entire organisation | Divisional teams manage access and ensure context-specific controls |
| Data processing | Centralised or distributed processing levels | Decentralised processing within domains |
| Use cases | Suitable for storing and analysing large volumes of different data | Suitable for large, complex organisations with different domains |
| Complexity of the implementation | lower, as the focus is on centralised storage and processing | higher, due to the decentralised approach and the need for coordination |
Data Mesh: Revolutionising data management. Discover decentralised agility and improved information sharing. How do businesses benefit? Learn more.
Introduction to Data Mesh: How companies benefit from decentralised data management
Data Lake Architecture
While the data lake itself is a component of a company's larger data architecture, it also has several components of its own:
- Data Ingestion LayerThe ingestion layer brings data from various sources into the data lake. In this way, companies can capture data from various sources, e.g. from structured Databasesunstructured logs, social media feeds, IoT devices and others.
- Storage levelThe storage layer stores all data in its original, raw form. It is designed to scale efficiently as data volumes grow, allowing organisations to create a flexible basis for storing different types of information.
- Data catalogue and metadata managementMetadata management helps users to understand, organise and access the big data stored in the data lake. The Data catalogue acts as an index that classifies and organises this data. Managing big data with this approach helps users to apply relevant schemas when retrieving it so that they can easily find and use the information they need.
- Processing and analysis layerThe data is converted and analysed in the processing and analysis layer. It supports various processing frameworks and batch processing, Real-time analyses and machine learning. In this way, users can gain insights and perform complex calculations with raw data.
- Security and administration layer: The security and governance layer ensures compliance with Data securitycompliance and governance guidelines. Encryption, access control, audit trails and data sequencing are essential to ensure that these standards are effectively met.
- Data access layerThe data access layer provides users and applications with easy access to data queries. The refined and reliable data is now available to end users via Business intelligence tools such as Tableau and Power BI. Through the use of APIs and query engines, users can retrieve data here regardless of its underlying format or structure.
- Management of the data life cycleThe Data Lifecycle Management layer monitors the entire lifecycle of the data in the data lake, including retention, archiving and deletion policies. It ensures that the data lake remains efficient and cost-effective by effectively managing storage and processing resources throughout the data lifecycle.
- User interface and toolsThe User Interface and Tools component was developed to make it easier for users to interact with the data. It offers user-friendly Dashboardsreporting tools and exploration interfaces that Data Scientistsenable data analysts and stakeholders to visualise and analyse information effectively.
As data lakes do not standardise, normalise, validate or transform data, some companies have started to use them differently. They use them as an improved staging area where data can land and be prepared before it is transformed and loaded into a data warehouse.
These factors make it clear why data lakes are no substitute for Data warehouses are not the same. Rather, they complement them. Data lakes can not only serve as a staging area, but also as an archive in which outdated data can be stored while remaining easily accessible for audits and historical analyses.
An architecture that uses data lakes and data warehouses together is also known as a Data Lakehouse is the term used. Such an architecture can store, process and manage unstructured, semi-structured and structured data in a single repository.
Data lakehouses offer companies an innovative solution for overcoming the problem of data silos and utilising the full potential of their data through a hybrid combination of data lakes and data warehouses.
Advantages of a data lake for companies
Data lakes offer several advantages such as
- Cost efficiency
- Centralised data repositories
- Robust analysis platform
Cost efficiency
According to Snowflake, one customer was able to save 75 % of data infrastructure costs by using data lakes. Data lakes minimise redundant processing by allowing data to be ingested directly without the need for extensive pre-processing. Users can convert data only when needed, saving valuable resources. A unified storage solution such as a data lake also helps companies to consolidate all data types in one central location, minimising pre-processing costs.
Centralised data repositories
Deloitte helped Nestlé build a centralised data lake that enabled the multinational food and beverage company to add $200 million in value over four years. Data lakes act as central hubs for company data, breaking down silos and facilitating team collaboration.
For example, a healthcare provider can consolidate patient records, lab results and billing information into a single repository. This integration enables different teams to access comprehensive data, which leads to better decision-making.
Robust analysis platform
Data lakes form a coherent core for data and analyses and equip companies for success in a data-driven world. With the help of special data warehouses, so-called data vaults, companies can decouple the storage and processing of data. This allows organisations to build more flexible and scalable data warehouses that are consistent with the schema-on-read model of data lakes.
According to AWS, Coca-Cola Andina was able to increase analytics productivity by 80 % with an AWS-powered data lake. Through the interplay of curated data vaults and powerful analytics platforms, data lakes can create a flexible analytics platform that can grow and adapt with changing data requirements.
Data warehousing is growing rapidly and is crucial for business decisions and data optimisation - read more about how leading companies are driving this sector forward in our article.
Examples of data lake solutions
There are several data lake solutions on the market, each tailored to the different requirements of companies. Here are five notable examples:
Amazon Web Services (AWS) for Data Lakes
Thanks to Amazon S3, AWS offers a robust data lake solution that is characterised by high availability, low latency and constant data access. The tool simplifies data management through seamless integration with AWS services such as AWS Glue and Amazon Athena. The scalability of the AWS cloud functions also enables companies to cope with growing data requirements without any loss of performance.
Cloudera for Data Lakes
Cloudera offers a hybrid data environment on-premise, in the public and private cloud. With enterprise-grade features such as single sign-on (SSO) and robust encryption processes, Cloudera Data Lake effectively equips organisations to securely and efficiently manage and analyse large volumes of data. Governance features such as authentication, security policy enforcement, authorisation and metadata management also give companies full control over their data assets. Cloudera also offers users the ability to integrate data from different data sources and tools.
Azure Data Lake Storage
Azure Data Lake Storage Gen2 enhances Azure Blob Storage with enterprise-grade security and granular access controls, making it a safe choice for data lakes. Azure gives organisations full control over comprehensive, large-scale data processing and storage with support for heavy workloads and operational storage.
Google Cloud Storage
Google BigLake is a powerful storage engine that seamlessly connects data lakes and data warehouses. With a unified interface for analytics and AI engines, Google enables organisations to query multi-format, multi-cloud and multi-modal data in a high-performance environment. At the same time, features such as fine-grained access controls allow administrators to precisely implement security policies at the table, row and column level.
In addition, the multi-cloud governance capability ensures comprehensive monitoring of data in different cloud environments, including Google Cloud, AWS and Azure. Finally, the integration of Data Lakes with BigQuery helps companies to use big data for rapid analyses.
IBM Cloud Object Storage
IBM Cloud Object Storage is designed to handle large workloads with high durability and security. It integrates seamlessly with IBM's data analytics and AI services and provides a robust solution for storing and managing large amounts of data.
A comprehensive look at business intelligence: how companies can make informed decisions and react quickly to market dynamics by analysing and visually processing data.
Maximising the potential of data lakes
Data lakes can be difficult, especially when it comes to storing raw data and managing large amounts of disparate information across multiple departments. However, organisations that are prepared to overcome these challenges with strict governance and access controls can reap the benefits. By establishing clear systems for organising and securing data, companies can transform their data lakes from potential 'data swamps' into valuable resources that drive business success.
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