A data lake is a centralised repository that allows you to store all your structured and unstructured data at any scale.

Data lakes store data in its raw, original form, and allow data engineers to conduct various types of analytics, such as:

These help business analysts make better and more data-driven decisions.

While technologies like our very own InterSystems IRIS® provide interfaces to interact with data lakes, the data lake itself is more of a conceptual design pattern for data storage than a software application with a traditional GUI.

When explaining a data lake to someone or demonstrating it, you'd likely log into one of these storage tools or services to show how data is organised, accessed, and analysed.

In this post, you'll learn everything you need to know about data lakes, including:

Let's jump in.

Often, data is stored in structured databases called data warehouses, where the data needs to be cleaned and structured before it can be used. While effective for periodic batch reporting, data warehouses are not as flexible for the needs of some real-time processing and analytics. They work well for some business applications, but require the data to fit into a predefined schema before being written into the warehouse.

Some businesses are gathering thousands of data points every day from dozens of sources (and that's on the low end). When these rarely share the same schema or format it can make refining raw data time-consuming and inefficient in a traditional data warehouse.

Data lakes, by contrast, accept data in raw form from various sources, including IoT devices, social media feeds, mobile apps, and more. Rather than having schema enforcement like data warehouses, data lakes usually utilize "schema-on-read" technology. This means data is only structured when it's read for analysis, giving data analysts much more agility and efficiency.

Data lakes are primarily used by data scientists and data engineers to extract meaningful business insights, which are then visualised for presentation.

Data lakes are designed with several key characteristics in mind:

• Scalability: they can scale massively to store petabytes of data, accommodating the exponential growth of data in modern enterprises.
• Flexibility: data lakes support various data types, from CSV files and images to binary data, without the need for converting into a predefined schema.
• Cost-effectiveness: by using inexpensive commodity hardware or cost-efficient cloud storage solutions, data lakes can be a more economical option for massive data storage.

These features make data lakes an indispensable asset for businesses looking to leverage big data analytics for deep insights and informed decision-making.

As we dive deeper into the architecture, implementation, and operation of data lakes, you'll soon see why they're pivotal in modern data management landscape.

When considering whether a data lake is the right solution for your organisation, stakeholders should ask a series of diagnostic questions to evaluate their current data capabilities, needs, and future goals.

Here are some crucial questions to guide this assessment:

Determine if your organisation handles large volumes of structured, semi-structured, or unstructured data. Data lakes are particularly beneficial for organisations dealing with diverse and large-scale data sets.

Consider whether existing data management systems (like traditional databases or data warehouses) are sufficient for your organisation's needs, particularly in terms of scalability, flexibility, and cost-effectiveness.

If you want to enhance your capabilities in predictive analytics, machine learning, or real-time analytics, a data lake might be necessary to handle and process large datasets dynamically.

Consider whether your organisation has the IT infrastructure and technical expertise required to implement and maintain a data lake. This includes evaluating the availability of skilled personnel and the need for potential training or hiring.

Determine the financial resources available for investing in a data lake. While data lakes can be cost-effective in the long term, setting up and maintaining a data lake may require significant initial and ongoing investments.

Assess how a new data lake will integrate with existing IT systems and business processes. Effective integration is crucial for maximizing the benefits of a data lake. By thoroughly considering these questions, stakeholders can make an informed decision about whether a data lake is suitable for their organisation’s needs. This strategic approach ensures that the investment in a data lake aligns with broader business objectives and technological capabilities.

If you've gone through these questions and believe you're a prime candidate for a data lake, here is a high-level overview of how to implement one.

Engage with stakeholders from risk management, customer service, and operations to gather specific data needs. Determine how the data lake will be used to conduct real-time risk analysis, provide personalised customer experiences, and streamline back-office processes.

Opt for a robust data management platform like InterSystems IRIS that can accommodate all the specific needs of a data lake architecture.

InterSystems IRIS is:

• Flexible, supporting the integration of data from multiple sources and data types
• Scalable, built to efficiently manage large scale datasets
• Interoperable, allowing seamless communication between different systems and applications

InterSystems IRIS provides robust security features, including encryption, authentication, and auditing, which are critical for managing sensitive data in a data lake. Additionally, it supports data governance practices to help ensure data quality and compliance with various regulations.

Include data from transaction systems, customer interaction logs, market data feeds, and regulatory reports. InterSystems IRIS can ingest data from a variety of sources, including relational databases, NoSQL databases, file systems, and even real-time data streams. This flexibility is crucial for data lake architectures that need to consolidate data from disparate sources.

Establish clear data zones such as raw for unprocessed data, trusted for validated and cleansed data, and refined for data ready for analytics.

InterSystems IRIS's ability to integrate seamlessly with analytics tools means that data stored in the data lake can be directly accessed and analysed using SQL and other querying languages. This direct integration helps bypass the need for moving data into separate analytical databases, thereby reducing complexity and enhancing performance.

Data lakes are essential for businesses dealing with large volumes of diverse data and needing sophisticated analysis capabilities. InterSystems IRIS stands out as a powerful platform that can help organisations effectively implement and manage data lakes, ensuring they can maximize the value of their data assets.