Data lakes are efficient and scalable solutions for storing structured, semi-structured, and unstructured data. They are designed to process and secure the stored data. These solutions can be on-premise, cloud-based, or hybrid.
This article will explain what data lakes are, explore their benefits and use cases, and discuss how cloud data lakes bring data storage and processing one step further.
Definition of a data lake
Data lakes are scalable and secure platforms for storing data. They allow enterprises to store any type of data, from various sources, and with full fidelity without the need to structure it. A data lake can store structured data, such as relational databases; semi-structured data, like CSV or JSON files; unstructured data, e.g. documents; and binary data, such as images or videos. The main goal of using a data lake is to provide a single source of data for the entire organization, ready to be processed and analyzed.
Data lakes allow the ingestion of data from various sources. This includes text, images, log files, or sensor data. In practice, it means you can aggregate data from social media, transactional systems, IoT devices, and monitoring systems in one easily accessible data storage solution.
Data lakes also provide different types of analytics features, including dashboards and visualizations. They are designed to accommodate the growing variety, velocity, and volume of data generated by modern businesses. They are a foundation for advanced analytics, machine learning, and other data-driven applications, allowing organizations to derive valuable insights from their data.
Read our previous article to learn more about big data.
There are four types of data lake solutions:
- On-premises data lake - this is a traditional type of data lake. It requires both hardware and software management, which requires physical infrastructure and limits capacity and scaling abilities.
- Hybrid data lake - a data lake infrastructure consisting of both on-prem and cloud-based solutions. Streaming data between these two types of data lakes requires additional expertise, but it can be beneficial for companies with special security requirements.
- Cloud data lake - cloud-based solutions are paid for under a subscription model. It ensures scalability and flexibility, allowing for swift operations.
- Multi-cloud data lake - this type of data lake combines data storage services from multiple vendors. This solution allows organizations to benefit from various services at the same time, but it requires greater expertise to ensure smooth communication between cloud systems.
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Data lakes vs. data warehouses
A term closely related to a data lake is a data warehouse. What these two solutions have in common is that they both store data, but they serve different use cases. A data warehouse is designed to enable efficient querying and reporting, for example, to create monthly sales reports, usually serving business professionals. Data warehouses usually rely on a predefined schema, and they don’t store unstructured data - it has to be cleaned, transformed, and integrated before being loaded into the warehouse.
On the other hand, a data lake is a more flexible and scalable storage system that stores raw, unprocessed data in its native format, allowing for more diverse and exploratory analysis. Unlike data warehouses, data lakes do not require a predefined schema, making them suitable for storing large volumes of raw data from various sources.
If you want to explore the differences in more depth, check out our previous article: data lake vs. data warehouse.
Data lakehouse
Another concept worth mentioning here is a data lakehouse. A data lakehouse combines the benefits of both data lakes and data warehouses. It represents a unified platform where organizations can store and manage vast amounts of structured and unstructured data in its raw, native format (similar to a data lake) while also providing structured processing capabilities and query performance, like data warehouses. This approach enables users to seamlessly analyze and derive insights from diverse data sources, supporting both batch and real-time processing. The term "lakehouse" reflects the integration of the scalable, flexible storage capabilities of a data lake with the structured, optimized querying features of a data warehouse, offering a comprehensive solution for modern data analytics and business intelligence needs.
How do data lakes work in the cloud?
A cloud data lake is a centralized repository storing all types and volumes of data using cloud-based storage and computing services. These solutions are provided by public cloud vendors, such as Amazon S3 (Simple Storage Service), Azure Data Lake Storage, and Google Cloud Storage.
Hosting a data lake in the cloud brings all the benefits of cloud computing to data storage and processing. Cloud data lakes offer scalability, flexibility, accessibility, and cost-effectiveness. The key benefits of cloud data lakes are auto-scaling and self-service access for users.
To understand how cloud data lake solutions work, let’s take a look at the data journey in such a solution.
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Ingestion of data
Data is collected from multiple sources and transferred to the lake raw without processing. Data can be ingested in batches or in real-time.
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Storage
All the ingested raw data is stored in a centralized repository. Cloud vendors ensure that data stays secure, preventing damage and maintaining data quality. At the same time, data scientists and other users can easily access the data from any location.
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Analytics
The final step is analytics, which is the end goal of any data-related project. A cloud data lake enables access to processed data and integration with business intelligence tools, which data scientists or other professionals can use.
The biggest advantage of a cloud data lake is that each element of the data journey can scale independently and according to requirements.
Metadata management in a cloud data lake
Metadata management in a cloud data lake is a crucial aspect of effective data governance and utilization. Metadata refers to the information that describes and provides context to the stored data. This includes details such as data source, format, schema, quality, lineage, and access permissions.
Efficient metadata management ensures that users can discover, understand, and trust the data within the data lake. It involves the creation, capture, storage, and maintenance of metadata, enabling data cataloging, searchability, and lineage tracking. Cloud-based metadata management solutions often leverage advanced technologies like machine learning to automate metadata tagging and enhance data discovery.
With well-managed metadata, organizations can streamline data governance, facilitate collaboration among data users, and enhance the overall efficiency and reliability of their analytics and business intelligence initiatives in the cloud environment.
Cloud data lakes vs. on-premise data lakes
There are various data lakes available, including on-premise and cloud-based solutions. They differ in several aspects, including infrastructure, scalability, cost, maintenance, and accessibility. Here are some of the main differences:
Infrastructure
The most significant difference between a cloud data lake and a traditional one is infrastructure. The first one utilizes computing resources provided by a cloud service provider. Infrastructure is managed and maintained by the cloud provider, allowing for quick scalability and flexibility. An on-premise data lake requires organizations to set up and maintain their own physical infrastructure, including servers, storage, and networking equipment.
Scalability
A cloud data lake offers easy scalability by allowing organizations to adjust storage and processing capacity based on demand. With traditional data lakes, scalability is limited by the physical hardware and infrastructure owned by the organization. Scaling up involves purchasing additional hardware, which can be a time-consuming and costly process.
Cost
Due to the above-mentioned issues with maintaining physical infrastructure and needing to expand it to scale, a traditional data lake requires substantial upfront investment in hardware and ongoing maintenance costs, including power, cooling, and personnel. A cloud data lake operates on a pay-as-you-go model, which provides cost flexibility.
Maintenance
Cloud lake providers handle maintenance, updates, and security. This reduces the burden on organizations for routine maintenance tasks. With on-premise solutions, organizations are responsible for the maintenance, which requires dedicated IT personnel and resources.
Accessibility
In the cloud, data is accessible from anywhere with an internet connection, providing flexibility for remote access and collaboration, as opposed to traditional data lakes, where accessibility is limited to the physical location of the on-premise infrastructure.
Security
Cloud providers implement robust security measures, including encryption, access controls, and compliance certifications. However, some organizations may have concerns about data security in the cloud. With an on-premise solution, organizations have direct control over the physical security of their infrastructure but bear the responsibility for implementing and maintaining security measures.
Ultimately, the choice between a cloud-based and on-premise data lake depends on factors such as organizational requirements, budget constraints, and the specific use case for the data lake. Many organizations opt for a hybrid approach, combining elements of both cloud and on-premise solutions to balance flexibility and control.
The challenges of data lakes in the cloud
Cloud computing is becoming more popular in many business domains. The variety of available cloud services makes cloud data management possible and viable for nearly any organization. However, there still are some considerations regarding these solutions. Here are some challenges you should consider when implementing cloud data lakes:
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Data management - Storing multiple types of data is an advantage, but it also comes with its downsides. Managing different data formats is a tedious and complex task that sometimes gets out of hand. Such instances are referred to as “data swamps”, meaning a data lake that is full of disorganized, impossible-to-navigate files. Organizations can avoid a data swamp by providing proper metadata management.
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Migration - transferring data to a cloud-based infrastructure can go wrong in many ways due to the volume and various types of data, resulting in damaged or low-quality data.
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Data governance - due to diverse data formats and sources, ensuring data quality and maintaining governance can be challenging. This can be overcome with frequent and thorough quality checks and robust governance policies.
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Storage costs - a pay-as-you-go subscription model is beneficial, but only to a certain point. The cost of storage grows over time, as opposed to on-prem solutions, which only require an initial investment and involve low-cost maintenance.
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Data security and compliance - some data might be too sensitive to be stored in a public cloud. However, security can be improved by implementing strong access controls, encryption, regular audits, and data access monitoring to comply with security and privacy standards.
Cloud data lakes, like any other solution, are not perfect, but all of the above-mentioned challenges can be overcome with a combination of careful planning, the use of appropriate technologies, and ongoing monitoring and optimization efforts. Organizations that successfully navigate these challenges can efficiently use their cloud data lakes for machine learning and big data analytics.
Data lake use cases
Data lakes are versatile systems that can be used in many industries for different purposes. A primary example is using aggregated data for training machine learning models, such as large language models. These models can be used by diverse companies. For example, media streaming services can use ML to provide better recommendations, an e-commerce business can analyze customer behavior and purchasing patterns, or a healthcare organization can train a model to support diagnostics.
Data lakes are also commonly used in monitoring and observability use cases, for example, storing and analyzing log files and event data for monitoring and troubleshooting. Technology companies can use a data lake to store log data from applications and servers, enabling efficient analysis for identifying and resolving issues.
Do you need a data lake?
To answer the question of whether or not your organization needs a cloud data lake, you should consider a few key aspects. This includes the type of data you work with, what sources it comes from, what kind of processing and advanced analytics you want to perform, and your data management and governance practices.
What should also be considered is the skill set of the professionals who will use the cloud data lake. Are they familiar with such solutions, or will they need help in the implementation and migration process? Answering these questions can help you assess what kind of data-storage solution best suits your business needs.
Conclusion
Cloud data lakes are about much more than storing data in a cost-efficient way. The goal is to provide a data source for analytics, resulting in valuable insights and better business decisions. Being data-driven is the key to success in the modern business landscape. Having access to all of the historical and real-time data an organization stores in one place provides you with more context than ever before, allowing you to get a step ahead of the competition.
