The digital universe is doubling in size every single year. And along with that comes the creation of more data than businesses know what to do with. Seriously. Consider these statistics:
- 1.7MB of data is created every second by every person during 2020.
- In the past two years alone 90% of the world’s data has been created.
- 2.5 quintillion bytes of data are produced by humans every day
- By the end of 2020, 44 zettabytes will make up the entire digital universe
But here’s the problem: A lot of that data is unstructured or semi-structured. This presents a two-fold challenge for many businesses. First, the need to find a way to store all of this data. Second, they need to find a way to maintain the capacity to process and analyze it quickly.
Fortunately, that’s where a data lake comes in. No longer just a technical buzzword, data lakes are increasingly becoming instrumental tools in helping businesses organize and make sense of reams of data that were previously disjointed, siloed, and near-impossible to analyze. As a result, more and more businesses can better access and derive useful insights into consumer behavior and the motives and strategies of their competitors. So if you aren’t tapping into the power of data lakes yet, what are you waiting for?
Here's a crash course on what data lakes are, why they’re so important for today’s businesses, and how you can implement one for your own data.
What is a data lake?
Coined by James Dixon, the CTO of Pentaho, the term “data lake” refers to the ad hoc nature of storing data, in contrast to the processed data stored in traditional data warehouse systems.
In short, a data lake is a centralized repository that is capable of storing high volumes of data from many sources. Data lakes are capable of storing both traditional structured data as well as unstructured raw data in its native format. Then, leveraging inexpensive object storage and open formats, applications can easily access and take advantage of this data to speed up data-driven innovation.
So how is a data lake used? Oftentimes, a business uses a data lake to consolidate all of its data in a single location, where it can be saved in its raw form (aka "as-is"). This means there’s no need to structure or process it upfront.
There’s one more thing to know about data lakes—and that’s understanding what it isn’t. Data lakes are often compared to their more traditional counterpart, data warehouses. Data warehouses are a storage repository that stores data extracted, transformed, and loaded into the files and folders. In contrast to data lakes, however, a data warehouse only stores structured data from one or more disparate sources.
Here’s a comparison of each:
Why do you need a data lake?
Today, companies are faced with a lot of data, but it’s often isolated and siloed, placed in different storage systems, like databases or data warehouses. Data lakes break down those silos, centralizing and consolidating all of your organization’s data into a complete, always-current solution. This unification of your data is the first step for companies looking to harness the power of machine learning and data analytics to stay competitive in the next decade and beyond.
Here are a few reasons why today’s businesses need data lakes:
It’ll help you tap into the power of data science and machine learning: You can seamlessly tap into the power of analytics, data science, and machine learning with the transformation of raw data into structured data via your data lake.
You can democratize your data: Data lakes are incredibly flexible, enabling users with completely different skills, tools, and languages to perform different analytics tasks.
You can centralize and consolidate your data: A centralized data lake eliminates problems with data silos (like data duplication, multiple security policies, and difficulty with collaboration), offering downstream users a single place to look for all sources of data.
You can quickly integrate diverse data sources and formats: Any and all data types can be collected and retained indefinitely in a data lake, including batch and streaming data, video, image, and binary files, and more. And since the data lake provides a landing zone for new data, it is always up to date.
Your team can analyze data faster: A data lake works on a principle called schema-on-read. This means that there is no predefined schema that your data needs to fit into before being stored. Only when the data is read during processing is it parsed and adapted into a schema as needed. This feature saves a lot of time that’s usually spent on defining a schema.
Where do data lakes live?
In general, you have two options: In the cloud or on-premises.
When data lakes are maintained on-premises, they require the combined management of both hardware and software. This requires more engineering resources and greater expertise, and it also locks companies into a static scaling solution, where they have to be sure to maintain capacity overhead in order to avoid any downtime as they expand storage.
Cloud data lakes, on the other hand, help overcome these challenges. You’ll benefit from availability, speed, and lower engineering and IT costs. This option will also allow you to operate swiftly, without having to measure every decision against expertise. Most importantly, a cloud-based data lake gives you a centralized location for all object stores and data access, and the setup is the same for every team in an organization. This simplifies operations complexity and frees up time for engineers to focus on more business-critical initiatives.
Are you ready to explore a cloud-based data analytics solution?
For enterprises that need help accelerating their data modernization journey, Velocity’s Cloud Data Analytics (CDA) offering will minimize the complexities of implementing a data lake and help enterprises achieve business-impacting results much faster than on their own. Utilizing cloud-native solutions, CDA will create a data lake solution that is flexible to handle new requirements and data, scalable to grow with the business, and resilient to handle strict SLA requirements.