7 Key Benefits of Proper Data Lake Ingestion

6 Min Read
Shutterstock Licensed Photo - By Stuart Miles

It’s impossible to deny the importance of data in several industries, but that data can get overwhelming if it isn’t properly managed. The problem is that managing and extracting valuable insights from all this data needs exceptional data collecting, which makes data ingestion vital. The following will highlight seven key benefits of proper ingestion.

1. Proper Scalability

Perhaps one of the biggest perks is scalability, which simply means that with good data lake ingestion a small business can begin to handle bigger data numbers. The reality is businesses that are collecting data will likely be doing so on several levels. Data is being collected through third parties, partners, social media accounts, and other sources. Having proper scalability capabilities means you’ll be able to handle the spikes in data.

2. Covering Data Types

Having the ability to ingest data from several sources is one thing, and that’s important, but your company must also be able to collect different types of data. You’d be surprised how many forms of data there are, such as logs, XML, sensor data, social data and data from chats. All of this information needs to be collected accurately, and that can be done with quality data ingestion. Choosing poorly could end up causing you to ingest data incorrectly, and that’ll create delays and potential losses that no one wants.

3. Capturing High-Velocity Data

There’s a lot of ways to store data once it’s captured, but one of the most popular ways has to be data warehousing systems. The problem with that specific type of data storing method is that this system isn’t too good at capturing high-velocity data. Today’s businesses are capturing this type of data all the time, usually from social media accounts where data is uploaded at lightning speeds. Having a tool like data lake could help capture this type of data much more efficiently, making sure you don’t miss a thing. Data lake uses things like Kafka and Scribe to be able to collect data at these speeds without suffering from machine exhaust.

4. Sanitizing Data

Once the information is ingested, the data has to be sanitized. This is pretty complex, but good data ingestion must ensure that this process happens flawlessly. What happens is that all data ingested is going to be cleaned, so duplication issues and manipulations should be eliminated. The process can be tweaked if you introduce scripts or by having a data expert help create more effective sanitizing processes. No matter how hard you try, small mistakes are going to be made with data collection, but you’re addressing that issue now.

5. Data Analytics Simplified

Part of the reason you would even need a process tool to help you collect and sanitize this amount of data is so that your data analyzers can help decipher all of the information being collected. It’s here where data exploration and insight extraction beings to take place. The information could be used to highlight your company’s overall standing, or maybe it could reveal issues your company is facing. Once the insights are clear, a business owner can create plans to improve the company to continue having an edge over competitors.

6. Stores in Raw Format

Once the information is processed and analyzed, you would imagine that the data would have to be stored in some pre modeled form. This is no longer something you have to worry about because data lakes are not like a warehouse. Being able to store all the data in its raw form helps ensure you have access to it later on without jumping through hoops. Of course, at this point, data is going to be properly tagged and organized for easy access, but being able to keep everything as is makes data lakes pretty powerful. Your business analysts could ask much more complex questions about the data collected long after that initial analysis.

7. Uses Powerful Algorithms

Your business analysts won’t be doing all this work on their own. A good perk to point out about data lakes is that they can use powerful algorithms to help your analysts comprehend the gathered data. The categories, the tags, patterns, and much more could all be recognized by deep learning algorithms, which is part of the reason why data ingestion is so effective. Part of the reason data is so easily simplified in this environment is that you’ll be using algorithms to get a lot of the work done.

Hopefully, some of this information helps you understand why data lake ingestion is vital and why it needs to be considered. It’s just the next step in data exploration, and there’s no telling what else is possible.

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