Data lakes have the potential to revolutionize your organization’s data warehouse and storage capabilities. However, to harness their full potential, it’s essential to avoid common pitfalls and embrace best practices. Based on our extensive experience building data lake architectures, we have identified several key insights and common mistakes. Here are the most critical ones to consider:

1. Develop Plausible Use Cases

While the allure of data lakes is strong, it’s crucial to have clear use cases that address specific business needs before implementation. Thorough use case analysis sets clear expectations for stakeholders and significantly boosts the chances of successful deployment and goal achievement.

 

2. Avoid Exporting Relational Databases to Files in the Data Lake

Ingesting normalized database tables like a snowflake schema into the data lake can cause performance issues and latency. Big data technologies are designed for large, flat, denormalized files, not small data files requiring foreign key joins. Focus on extracting and working with larger, flat files for better performance.

 

3. Prevent the Creation of Data Pond Silos or Data Swamps

Smaller repositories often emerge from different groups ingesting varied data or using different processing schemas. This fragmentation can lead to data silos and swamps, making it difficult to maintain data consistency, quality, and coherence. Avoid these pitfalls by planning for the big picture, agreeing on schemas, and maintaining detailed metadata. Train staff across departments to use and maintain a unified data structure.

 

4. Automate Installation and Configuration

Manual configuration management may seem quicker initially but doesn’t pay off in the long run. Use IT automation tools like Docker Machine, Rancher, or Chef to streamline maintenance and support. Embracing microservices architecture can further improve efficiency by allowing your data lake to be developed as a suite of small, independently deployable services.

 

5. Do Not Use RAID for Data Nodes

Hadoop and similar big data technologies already stripe data across multiple nodes for fault tolerance and performance. Adding RAID on DataNodes creates unnecessary duplication and degrades performance. Instead, use a JBOD (Just a Bunch of Disks) approach for DataNodes and reserve RAID for NameNodes, which are single points of failure.

 

6. Avoid Logical Volume Management for Data Nodes

Logical volume management can degrade performance by adding an extra layer between the filesystem and the disk. This is particularly problematic for DataNodes, where performance is critical. Stick to simpler disk management approaches and avoid unnecessary complexity.

 

7. Steer Clear of SAN/NAS for Data Nodes

Using SAN or NAS storage with Hadoop contradicts the best practice of moving computation to the data. SAN/NAS setups create single points of contention and increase network hops, leading to performance bottlenecks. Instead, use local disks on commodity hardware to keep computation close to the data.

 

8. Optimize Infrastructure with Containers and Bare Metal

While virtual machines and containers both have their uses, for optimal performance, DataNodes should be on bare metal. This approach maximizes performance while still allowing the use of containers and DevOps tools for other parts of the infrastructure. Many cloud vendors now offer bare metal options, which can provide the best of both worlds.

 

Conclusion

The debate between data lakes and enterprise data warehouses often misses the point that both have their unique strengths. Data lakes offer new and incremental value, particularly when starting without an existing central repository for data intelligence. However, they may lack some of the mature features of traditional data warehouses. A polyglot approach, integrating both data lakes and data warehouses, can often provide the best solution, leveraging the strengths of each.

Data lakes are proving to be invaluable tools, surpassing the traditional, manually curated data warehouses. They offer more extensive storage and processing capabilities at lower costs, adapting to the ever-changing data intelligence and analysis needs of users. Embrace the future of data storage and management by implementing these best practices in your data lake strategy.

This post concludes our series on data lakes. If you missed the earlier posts, here are the links:

• Defining Data Lakes
• Data Lake vs. Data Warehouse
• The Value of Data Lakes
• Data Warehouse Integration

 

Is your organization missing out on valuable insights because of limitations in your existing data warehouse? By integrating a data lake with your current data warehouse, you can unlock powerful analytics that were previously out of reach.

Modernizing your enterprise’s data management with a data lake is highly recommended due to its immense value. However, this doesn’t mean you need to replace your current data warehouse. Data warehouses are reliable assets that can be integrated into a data lake architecture to enhance their value. Today, data management environments are moving towards integrated approaches that leverage the strengths of both data warehouses and data lakes.

Adding a data lake can extend the functionality of your data warehouse while respecting the investment you’ve already made. Using both systems allows your data scientists and developers to choose the best option for storage, processing, and analytics.

Here’s an overview of common approaches to integrating a data lake with your existing data warehouse architecture:

Ingest and Process in the Data Lake

In this approach, all data is ingested and stored in the data lake, serving as the initial staging area. Cheaper computing resources then process the data, and the results can be saved to the data warehouse, while the raw data remains in the lake. This method leverages the cost-efficiency of data lakes for processing tasks.

Warehouse as a Data Source in the Data Lake

Another strategy is to process all data, including data from warehouse sources, using data lake resources. This provides a standardized interface and processing API for all data repositories in your enterprise. Your team can learn one technology for most data processing needs, using tools that run massively parallel SQL queries while integrating with advanced computation and algorithm libraries. However, it’s crucial to understand the performance trade-offs, as not all SQL queries are efficiently executed on a cluster or grid of nodes.

Data Warehouse for Reporting

Keep your data warehouse as the core platform for standard reporting. Significant testing and due diligence ensure the accuracy of the queries and computations for these reports, especially for financial or other critical information. A common practice is to store raw data in the lake, process and transform it using lake resources, and then store the refined data in the data warehouse. This way, your reports and dashboards remain unchanged and accurate.

Archive Data

Use the data lake to store archived data that once resided in the warehouse. This allows you to store data at a lower cost using commodity hardware while maintaining access to historical information.

By integrating a data lake into your existing data warehouse infrastructure, you can significantly enhance your data storage capabilities and analytics potential.

In our next and final article in the data lake series, we’ll cover best practices in data lake architecture. If you missed the first three articles, check them out:

• Defining Data Lakes
• Data Lake vs. Data Warehouse
• The Value of Data Lakes

Is your organization looking to lower data storage costs while enhancing analytics? Data lakes offer a solution that reduces long-term storage expenses and improves data analysis and communication.

Even if your organization doesn’t need massive storage, a data lake is a valuable addition to your IT infrastructure, especially if you already have an Enterprise Data Warehouse (EDW). Let’s explore the significant benefits data lakes can bring to your IT landscape.

 

Expanded Storage Capacity

Data lakes allow enterprises to store all types of data in any format. In the past, storing all your data in a central repository wasn’t feasible due to cost and technical limitations. Data was often summarized, with detailed information deleted to save space. With a data lake, all detailed data can be stored indefinitely, enabling in-depth analysis without losing any fidelity. This comprehensive retention is cost-effective and empowers extensive data exploration.

 

Cost Efficiency

Previously, data storage and analytics solutions were costly and less sophisticated. Today, thanks to competitive cloud pricing, elastic compute and storage options, IT automation, and efficient resource utilization, storage and processing costs have significantly decreased. Although the initial investment in a data lake infrastructure may be higher than an EDW, the long-term savings are substantial. A smaller team can manage the growing data efficiently, leading to higher cost savings per byte as your data scales. Data lakes allow your technology to grow, not your team size.

 

Enhanced Data Integration and Omni-Channel Experience

Unlike traditional warehouses, data lakes can store all kinds of data, structured and unstructured. For years, unstructured data was stored separately and couldn’t be analyzed cohesively. Data lakes centralize all data formats, allowing for quick and cost-effective collection, processing, and analysis. They integrate internal, external, client, competitor, social, and business process data, enabling data engineers to extract critical metrics. This integration provides a clearer understanding of how decisions impact customers, suppliers, competitors, partners, and other stakeholders.

Today’s businesses compete for customer attention and dollars across multiple channels like search engine marketing, email, websites, blogs, and social media. Are you providing a seamless omni-channel experience for your customers?

An omni-channel strategy considers every platform, interaction point, and device a customer uses. It tracks the customer journey from the first touchpoint to purchase, ensuring a seamless experience across physical stores, online desktops, mobile devices, and phone interactions. Data lakes excel in capturing every detail of these interactions, enabling a comprehensive view of each customer. This level of detailed experience management can be extended to suppliers, partners, and other value chain players.

 

Conclusion

Data lakes offer immense value to your organization, from cost savings to enhanced customer experiences. In our next post, we’ll discuss how to pair data lakes with existing data warehouses for even greater benefits.

If you missed our earlier posts, catch up here:

• Defining Data Lakes
• Data Lake vs. Data Warehouse

In today’s fast-paced digital landscape, managing the volume, variety, velocity, and veracity of data is essential. To stay competitive, organizations need a highly agile data management system. This is where the distinction between data lakes and data warehouses becomes crucial. In this second installment of our data lake series, we’ll explore the fundamental differences between these two storage solutions.

If you missed our first article on defining data lakes, be sure to check it out.

Both data lakes and data warehouses serve as central repositories for integrated data from various business intelligence and operational systems. While they might seem similar at first glance, they have significant differences when you look closer. Here are the key distinctions your organization should be aware of:

 

Structure

Data warehouses use a pre-defined, structured format for storing data, typically in relational tables with rows and columns. This structure is defined in a schema before any data is stored.

On the other hand, data lakes are more flexible. They can store unstructured data (like plain text and emails), binary data (such as images and videos), semi-structured data (like logs and JSON files), and structured data from relational databases. The schema in a data lake is defined at the time of data reading, not writing, allowing for greater flexibility in data storage.

 

Data Extraction

Data warehouses are designed for easy access by analysts and users with less technical expertise. Data lakes, however, require data professionals and scientists to extract value due to the vast amount of data, its varied formats, and the rapid evolution of information.

 

Velocity

Data lakes excel in the speed at which they can ingest data. Their distributed design allows them to scale out, enhancing ingestion speed. This capability supports use cases like real-time sentiment analysis from social media streams or quality control from manufacturing sensors.

 

Agility

Data warehouses, with their schema-on-write characteristic, are less agile in adapting to changing business needs. Any changes to the data structure require significant adjustments to the ingestion processes, ETL (extract, transform, load) pipelines, and APIs or visualizations.

In contrast, data lakes’ schema-on-read approach provides unmatched flexibility. Enterprises can store data in its native format and later repurpose it for different analyses without altering the core architecture. This adaptability supports ad-hoc analysis and data discovery, allowing data professionals to explore and derive insights driven by their train of thought.

 

Costs

While data warehouses can handle large amounts of data, their relational database model isn’t optimized for distributed hardware architectures. This can lead to performance and latency issues, making it expensive to scale up. Data lakes, however, benefit from open-source big data technologies that allow horizontal scaling with cost-effective hardware.

 

As data lakes become more prevalent, their security measures are also improving, often surpassing those of traditional data warehouses. Considering their agility and cost-effectiveness, data lakes are becoming the preferred storage option for many organizations.

In our next article, we’ll delve deeper into the value that data lakes bring to the table. Stay tuned!

Does your organization rely on data to make critical business decisions? If so, adopting a data lake architecture can help you maximize the value of your data sources.

Understanding Data Lakes

The enterprise information management market is constantly evolving, and staying ahead of these changes is crucial for any competitive organization. With the advent of affordable storage hardware and open-source software, organizations can now store more data and do more with it by integrating data lakes. Traditional data warehouses, while useful, can be inflexible when it comes to analyzing and incorporating new data formats. This rigidity might cause organizations to miss out on valuable insights, leading to less informed decisions and missed opportunities. To avoid this, modernizing data systems to handle all data formats is essential, and incorporating a data lake should be a key part of this strategy.

What Is a Data Lake?

A data lake is a storage management system capable of handling unstructured, semi-structured, and structured data in one repository. This means it can store, adapt, and analyze data in its native format with minimal prerequisites. Unlike traditional storage technologies that require specific data formats, data lakes allow data to be easily queried for various organizational needs. The flexibility of data lakes means that data types (such as email, date, number, etc.) don’t need to be defined until the data is queried, offering a more adaptable approach to data storage.

Data Lakes vs. Traditional Data Warehouses

Traditional data warehouses are structured systems bound by strict storage rules, requiring precise definitions for each table column. This rigid framework makes it difficult to integrate new data formats without significant adjustments. As data formats and attributes continually evolve, this rigidity can prevent organizations from making the most of their data. However, by integrating a data lake with your existing enterprise data warehouse, you can enjoy the best of both worlds: the flexibility of a data lake and the structured reliability of a data warehouse.

What’s Next?

In the coming weeks, we’ll explore:

• Data Lakes vs. Data Warehouses: A detailed comparison.
• The Value of Data Lakes: How they can benefit your organization.
• Integration Strategies: How to seamlessly integrate data lakes into existing data warehouses.
• Best Practices: Tips for successful data lake integration.

Stay tuned as we dive deeper into each of these topics, helping you optimize your enterprise information management and make the most of your data assets.