Do you have the enviable problem of your business being so successful, you need more data storage space? AWS-powered data lakes have the broadest portfolio for scalability coupled with the highest security and cost-effectivity required to build and manage a data lake for analytics.
As companies grow, it’s not unusual for many of them to turn to data lakes for centralized, secure data storage rather than leaving it scattered in silos.
Data lakes are repositories to store large amounts of unstructured, structured, and semi-structured data for multiple types of use cases predominantly used by business analysts and data scientists. They’re also highly flexible and scalable, helping to improve operational efficiency.
AWS-powered data lakes have the broadest portfolio for scalability coupled with the highest security and cost-effectivity required to build and manage a data lake for analytics. Once data is ready for the cloud, AWS makes it easy to store data in any format, provides consistent security for multiple analytics, and at massive scale. Backup and archive solutions mean you can keep costs down even when doing the heavy data lifting.
Here are a few benefits you’ll experience as a result of using data lakes:
Data warehouses are another popular storage option, however they starkly differ from data lakes in that they require data to be structured into a schema before it can be stored. This is to optimize it for fast SQL queries for operational analysis and reporting by business analysts. Structuring the data properly requires careful planning and design, including selecting which data you’ll need in the future rather than simply keeping the data you might need. This can make them less agile to configure.
Since the data has already been structured for pre-defined operational purposes, its uses may be limited. Many organizations that already use data warehouses have been adopting data lakes as well to accommodate more diverse queries and data science use cases, as data lakes offer flexibility. The raw data it captures can be later transformed into the format you need when business questions come up, or just left the way it is.
Data lakes are also designed for low-cost storage, whereas data warehouses can become quite costly at larger volumes.
Other forms of storage use an Extract, Transform, and Load model, whereas data lakes go by Extract, Load, and Transform. Not having to transform the data lowers the time it takes to ingest it, which means anyone who needs it doesn’t have to wait as long for access. Being able to access data with a smaller delay from its time of capture can help support your organization in making more timely business decisions, especially if you’re in an industry where data fluctuates frequently, such as finance.
Data lakes are wide open to accept data from a wider array of sources as opposed to data warehouses and legacy systems. Their power combines customer data from CRMs, social media, and customer incident reports. Discovering patterns (or the lack thereof) across multiple platforms, even if they seem disparate, can help you address any customer demographics or psychographics that need any extra attention.
Overall, more raw data from more sources can open up perspectives you wouldn’t have had otherwise. You’ll be able to process the data for machine learning, predictive analysis, and data profiling.
Data lakes can open up a whole host of new opportunities and greater efficiency for an organization with growing accounts. Just don’t take too much advantage of the low costs and flexibility and turn your lake into a swamp. Loading way too much data will constrain bandwidth and disk space, leading to more latency.
And for efficient and reliable access to your data, take advantage of the analytics and machine learning solutions built by APN partners such as SM Innovations specifically for data lakes. Drop us a line whenever you're ready to transform your data storage!