概要
Explore data lakes and how they’re used, including industry use cases and important data lake elements. Next, review the value and benefits of a data lake.
Finally, learn about data lake architecture, challenges, and best practices, and understand the differences between data quality, data integrity, and data cleansing as they relate to data lakes.
A data lake is a centralized storehouse that enables organizations to store and manage enormous amounts of data in its native format—whether it’s structured, semi-structured, or unstructured—in a single, secure, managed environment that’s easily accessible across the enterprise.
Data lakes are equipped to intake, process, and analyze data in a variety of programming languages from any system at scale, in both real time and batch mode. They’re commonly used as a foundation for analytics projects that require vast quantities of data, which can then be used to power data exploration, data science, analytics dashboards, machine learning (ML), and other data-driven initiatives.
This allows organizations to streamline collaboration across teams, unlock insights from their data faster and more efficiently, and accelerate and enhance decision-making and innovation.
The ability of data lakes to store and analyze data at scale allows companies to drive breakthrough artificial intelligence (AI) and machine learning insights and innovation securely and cost-effectively. Not only can this drive revenue, but it can also significantly enhance the customer experience, streamline data connectivity, and improve operational efficiency.
By using real-time data, banks can analyze customer preferences and financial needs, then recommend relevant products, such as loans, credit cards, and investment opportunities to increase customer loyalty and boost revenue.
An online retailer can capture and analyze digital interactions at multiple touchpoints to create more tailored experiences, ensure inventory levels, and solve for gaps and breakdowns in the customer journey.
By eliminating data silos, healthcare organizations can create more connected patient care journeys, from doctor’s visits and rehabilitation services to imaging and prescriptions, to better track treatment effectiveness and improve patient outcomes.
Real-time data analytics can help airlines optimize flight schedules around factors such as aircraft availability, crew scheduling, airport capacity, and demand patterns. Then they can generate schedules that minimize turnaround times and maximize fleet utilization, leading to reduced operational costs and improved customer satisfaction.
A well-designed data lake includes several important factors that effectively support various data processing and analysis tasks. These include:
Data lakes have the capability to ingest data in its original format from a variety of sources, including databases, logs, streaming data, external APIs, and more—and store them in a single repository. This eliminates data silos and costly data duplication, allowing organizations to import data in real time and effortlessly scale their data needs.
A data lake provides cost-effective, scalable storage, for a variety of data without the need for extensive preprocessing or transformation. This includes everything from relational databases and JSON and XML files to images, videos, and text documents. Plus, it includes metadata that allows you to clearly see where the data came from and helps users more easily find the data they need.
Data lakes often integrate with processing and analytics frameworks, allowing users in different roles throughout your organization to easily access and leverage the data. This lets them quickly explore, experiment, and prototype with vast amounts of information, encouraging more accurate and informed decision-making.
Data lakes clean, aggregate, and merge datasets to prepare them for use in training ML models. They also transform data in ways that can predict model outcomes and improve the overall performance of models, leading to faster innovation and reducing time to market for new products and services.
Data lakes need to implement robust security and governance measures to ensure data privacy, compliance, and data access controls, including encryption and data monitoring. They also should support compliance with data privacy regulations and industry-specific standards and include auditing capabilities to track access and changes to the data.
Harnessing data to drive innovation is at the heart of what makes a data lake so valuable to organizations. The connectivity of a data lake powers collaboration, reveals new opportunities, and unlocks more value across the enterprise—all while minimizing data movement. Here are some specific examples of the benefits of data lakes.
Data lakes allow you to build robust ML models and analyze them more quickly regardless of the use case, from product sales forecasting and real-time fraud detection to dynamic supply chain management, anticipating customer behaviors, and accelerating drug discoveries.
A data lake can seamlessly analyze customer information to improve the customer experience and overall buying journey. This can include leveraging historical data to develop hyper-contextualized customer experiences, uncover underlying causes of customer churn, develop personailzed offers and incentives, and increase customer retention.
Data lakes are highly scalable, so organizations can efficiently store and manage vast amounts of data. Plus, companies can expand their storage and processing capacity as data volumes grow, without having to implement major architectural changes.
Using cost-effective, integrated cloud-based storage solutions and automated data lifecycle management, data lakes eliminate expensive data movement, reformatting, and unnecessary retention—improving data accuracy, ensuring data security, scaling operational efficiency, and accelerating innovation.
To get an idea of data lake architecture, it’s helpful to understand the difference between a data lake and a data warehouse. While both store data, they do it in different ways and for different purposes.
You can think of a data warehouse as a sort of corporate memory comprised of transactional systems and other related business applications. It retains current and historical company data, which is treated and formatted for a specific purpose and then accessed via structured query language (SQL). This data can then be used to generate snapshots of information, such as sales reports, marketing KPIs, and website traffic.
A data lake can capture and store data in real time from a wide range of sources—including business applications, mobile apps, internet of things (IoT) devices, and more—without having to format or define it first. This is referred to as “schema on read.” It allows data lakes to hold vast amounts of raw data and easily scale as an organization’s data requirement change. data flexibility lets users easily access it in various ways without having to know SQL. The data can then be used in advanced analytics and machine learning models to better predict future business outcomes and unlock key insights.
Often, organizations will implement both data warehouses and data lakes to complement each other and their differing data needs.
| Date lake | Data warehouse | |
| Data type | Relational and non-relational; can be structured, semi-structred, and unstructured | Relational and structured |
| Schema | On read | On write |
| Use cases | Machine learning, advanced analytics, forecasting, real-time analysis | Core/batch reporting, data visualizations, business intelligence |
| Primary users | Data scientists, data, developers, engineers | Business analysts |
| Scalability | Can scale easily and cost effectively | Difficult and expensive to scale |
| Cost | Storage and data management costs less than with a data warehouse | More data management is required, and storage costs more than with a data lake |
Data lakes can drive significant business value. But they do pose some hurdles. Many data lakes lack built-in support for key functions, including access controls, consistent data quality, and enforcement of data governance. Without processes in place to index, tag, and secure the data, information in a data lake can become murky and hard to access. That can lead to what’s referred to as a “data swamp.”
Data lakes often ingest data in its raw form, which can lead to issues with data quality. Inconsistent, incomplete, and inaccurate data can affect the reliability and credibility of analytical and machine learning results.
While data lakes can intake raw data, organizations often need to implement complex extract, transform, and load (ETL) processes to prepare that information for analysis. That can be resource intensive. Data lakes can also become slow and inefficient without properly optimizing query performance. That’s especially important as the volume of data grows.
It can be difficult to update or delete data in a data lake. That can make it challenging to maintain data governance and ensure data privacy, security, and regulatory compliance.
Many organizations look to a lakehouse to solve their data lake challenges. A lakehouse combines the intake, scalability, and analytic capabilities of a data lake with the data integrity and robust information management of a data warehouse. That allows enterprises to have a “single source of truth” for their data, but still leverage advanced analytics and machine learning to maximize business value—all in one solution.
A lakehouse can automatically integrate various data sources without having to make them fit formats or schemas. It can store and process data in a single platform accessible to innovators across the enterprise. And it can accelerate innovation while boosting data security, governance, and versioning.
Start with well-defined business and data goals. Understand what data you need to collect and how it will be used to drive value for your organization.
Create a data catalog that makes it easy for users to discover and understand the data stored in the data lake. And implement a robust metadata management system to document data assets. Metadata should include data sources, data lineage, data quality, and data descriptions.
Implement robust security measures, including encryption, access controls, and monitoring to protect data from unauthorized access and breaches, especially for sensitive data like personally identifiable information (PII). To comply with the General Data Protection Regulation (GDPR), PII must be concealed to save it indefinitely.
Invest in training and skill development for personnel working with the data lake. Data engineers, data scientists, and analysts should be proficient in data lake technologies and best practices so your organization can realize the most benefits.
By pairing the flexibility, scalability, and cost-effectiveness of a data lake with the information integrity of a data warehouse, a lakehouse can drive increased performance with harmonized data, while decreasing data redundancy and reducing storage expenses.
Data quality and data integrity refer to different aspects of data management. Data quality relates to the overall reliability, accuracy, and completeness of data in determining whether it is fit for use in analysis and decision-making. Data integrity focuses on ensuring that data is secure from breaches and that it remains accurate and reliable in the long term.
Data quality relates to the fitness of data and issues that may affect data accuracy, reliability, and completeness. Data cleansing is an area of data management that focuses on identifying and correcting errors and inconsistencies in data, such as missing values, duplicate records, and inconsistent formatting. It is used to improve data quality.
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