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What Is a Data Lake, and Who Needs It?

Data is a precious commodity, but only if you know how to use it. A data lake is one way to store large quantities of data that can be too expensive or inconvenient to keep anywhere else. But what is a data lake, and who needs it? In this comprehensive guide, we’ll introduce data lakes, cover some critical use cases and benefits, and describe the challenges you’ll likely face and how to overcome them.

What is a data lake?

A data lake is a large, centralized storage repository that can hold vast amounts of raw, unstructured data for later use by analytics, data processing, and machine learning applications. Unlike a data warehouse, which uses hierarchical dimensions and tables, a data lake uses a flat architecture to store data in file or object storage. That means you can collect and store data without needing to structure or organize it all, which allows you to work efficiently with massive amounts of data.

Data lakes can store a combination of unstructured, semi-structured, and structured data; This allows you to collect raw unprocessed data and data sets that have already been analyzed or categorized. This data is stored in a centralized location where it can be easily accessed by your data analysis applications, data scientists, and machine learning programs.

Traditional data lakes typically use Hadoop file systems to store and process data in a cluster of distributed computing nodes. However, newer cloud-based systems like Nodegrid Data Lake are built on cloud object storage services instead of Hadoop. Cloud-based data lakes provide the same benefits and functionality as traditional systems, but with easier cloud integrations and greater scalability and availability.

Why your business needs a data lake

Data is one of your most valuable assets. Data lakes empower you to harness your data and put it to work for your enterprise. For instance, a data lake can help your business:

Improve operational efficiency. Using business intelligence (BI) software with your data lake means you can automatically analyze and visualize your historical, current, and predictive operational data and find ways to optimize your processes to increase efficiency.
Improve the customer/client experience. You can use a data lake to store data from your CRM (customer relationship management), eCommerce platform, and incident response system. Then, with the right analytics, you can spot purchasing patterns, identify common pain points, optimize your customer service strategy, and more.
Break down data silos. A data lake keeps all your business’s data in one centralized repository, allowing your people (e.g., data scientists, business analysts) and your technology (e.g., machine learning tools) to get a complete view of available and relevant data.
Escape vendor lock-in. Since data lakes can handle raw and unstructured data, you can use them with various data sources, analytics platforms, and other systems. Then, you can choose the vendors who offer the best features, functionality, and pricing without worrying about compatibility with your data lake.

Data lake use cases and benefits

One of the best things about a data lake is that you can still benefit from one even if you don’t have a clearly-defined use case yet. You may have a lot of devices and sensors capable of collecting data, but you haven’t determined how you want to categorize, structure, or analyze that data yet. Collecting it now means you’ll have historical data to use when your analysis systems are in place, so you don’t want to miss out on it. However, storing it on a regular file server or database—or even in a data warehouse—would be unfeasible because of both the sheer volume of data and because you’d need to structure and organize it first.

Protection network security computer data and safe

With a data lake, you can capture all that data and store it in a flat architecture for later use by whatever analysis, machine learning, or big data processing applications you want to implement. Data lake storage is cheaper per byte than data warehouses, so you can consume as much data as you need to without worrying about soaring costs. And, since you can keep data in its raw, unstructured format, you have the freedom to work with any analytics, machine learning, or data discovery vendors you want without worrying about compatibility issues.

Essentially, a data lake lets you start collecting all your valuable data even if you don’t have a fully developed plan for how you’re going to use it. However, data lakes are also beneficial if you already have a use case for data collection and analysis.

Migrating legacy systems to the cloud

There are many benefits to migrating your legacy systems and services to the cloud, but you’re also likely to hit some roadblocks. One issue your enterprise may face is dealing with the vast amount of old data that hasn’t been organized or handled in years. You don’t want to delay your migration by taking the time to sort through all the data to find the important stuff, but you also don’t want to accidentally delete anything critical. You also can’t just leave that data sitting on a legacy server for no purpose without wasting valuable resources. This can be incredibly daunting if you’re in an industry with strict data retention regulations like finance or healthcare.

A data lake solves this problem by giving you an affordable, centralized repository to house all your legacy data. You can migrate your critical data and resources to the cloud, and then move the rest to a data lake. Then, when you’re ready, you can connect a data discovery and analysis tool to help you sort, classify, and use that old data as needed.

For example, imagine a law firm wants to migrate its legacy exchange email server to Office 365. It would be too expensive to store 20 years of old emails and attachments in their cloud email service, but they also can’t just delete them all because of the state bar association’s data retention rules. So, they purchase an affordable cloud solution like Nodegrid Data Lake to house anything more than a year old. Then, when they’re ready, they can implement a cloud-based data discovery tool that integrates with both their data lake and their Office 365 email so they can easily retrieve data about clients or cases no matter where it’s stored.

Nodegrid SR

One of the most popular use cases for a data lake is the storage of IoT (internet of things) data for later analysis. Your IoT devices collect a colossal amount of data, and you likely filter out most of it because you simply cannot store and process it all. That data may not be critical to your business operations, but by ignoring it you could be missing out on major issues—or key opportunities.

For example, the oil and gas industry was one of the early adopters of IoT technology. Since many offshore oil and gas production occurs in fairly dangerous and extreme environments, companies struggled to safely monitor their critical equipment and track important production metrics.

IoT sensors connected to LTE or satellite internet have enabled oil and gas companies to monitor and collect data from off-shore equipment without putting human beings in harm’s way. Then, with the help of a data lake, they can store and analyze that data in near-real-time. For example, IoT acoustic sensors can continuously monitor oil or gas flow rates within the interior of pipelines, and feed that information to a data lake where it can be analyzed to look for problems or areas for optimization. 

Using IoT (internet of things) devices with a data lake allows oil and gas companies to spot problems that may otherwise go unnoticed, so they can proactively fix or replace key machinery and prevent the issue from growing larger in the future. In addition, they can analyze their historical data to look for opportunities to explore additional drilling sites, lower their operating expenses, stay ahead of regulatory requirements, and more.

The potential use cases for a data lake are endless, and different industries may use them in completely different ways. Simply put, if your business generates and/or collects a lot of data, then you have a use case for a data lake.

Data lake challenges and how to overcome them

Though a data lake can provide many benefits for your business, there are still some pitfalls you should know about so you can avoid them
Data swamps: A data swamp is the result of a poorly configured or managed data lake. Though a data lake doesn’t require hierarchical tables or organization like a data warehouse, you still need some methodology for storing and accessing your data. If you just indiscriminately dump all your data without any folder structure or documentation, your developers won’t know how to write queries to find that data later.
Security: Storing all your raw data in one centralized location can be a security risk, especially one that must allow so many integrations from various data sources and analytics tools.
Data inconsistency: Since most data comes into the data lake unprocessed and uncurated, there’s no “single source of truth.” That means, when inconsistencies arise, you may struggle to determine which data to trust.
Luckily, there’s an easy way to overcome all these data lake challenges: choosing the right vendor.

Nodegrid data lake

Nodegrid Data Lake is a fully-featured and entirely cloud-based solution to help you store, manage, and analyze your data. Nodegrid doesn’t just house your data, but it also provides visualizations on six critical data points, including:

Infrastructure Environmental Factors
Power, cooling, relay, and dry contact sensors Temperature, humidity, and airflow sensors
Application Logs Networking
User experience data from Office 365, Zoom, point of sale, and other apps Data traffic, application profiling, and antenna/tower traffic
Security System
System logs, data logs, GPS data Disk usage, processes, and memory
Plus: Previously hidden server and switch logs from IPMI and RS232 serial consoles

Nodegrid’s intuitive, cloud-based interface helps you avoid data swamps with built-in searches, query builders, and data visualizations. Using cloud authentication and the Zero Trust Security Framework means you can access your data lake from anywhere in the world while keeping it secure. Nodegrid Data Lake’s powerful features and functionality ensure you have one single source of truth for all your valuable data.

Plus, Nodegrid is available as a complete solution that includes:

Environmental sensors that collect data on the conditions in your rack and feed them to the Nodegrid Data Lake
Serial console servers so you can view and manage your critical remote infrastructure
ZPE Cloud to consolidate your infrastructure management into one convenient, cloud-based platform

What is a data lake, and how can it benefit your business? Activate your free 90-day trial of Nodegrid Data Lake to find out!