Companies need to store and retrieve data for daily processes and other needs. Ideally, the data storage atmosphere using ELT is a data lake or an organized storage system that makes logging, storing, and retrieving data efficient and easy.

However, when all the ingredients necessary to have a data lake aren't in place, the storage atmosphere becomes what many refer to as a "data swamp." They then become difficult or impossible to retrieve the data they're looking for because it's not easily identifiable by a query.

Factors related to ELT usage and protocols which should create a tiered storage structure, begin lacking in an organization can quickly turn any data lake into a data swamp. When this occurs, it makes efficient data storage and retrieval difficult, if not impossible.

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Table of Contents

  1. ETL to ELT - Data Lakes Make it Possible
  2. What Is a Data Swamp?
  3. How ELT Creates Data Swamps
  4. What Are Data Lakes, and How do They Function?

ETL to ELT - Data Lakes Make it Possible

ELT is in reference to Extract, Load, and Transform. This process is a development or updates from the previous version of data extraction ETL (Extract, Transform, and Load). This process uses data stored in the form of a data warehouse.

Before the data needed to be transformed before being loaded into a database — it was extracted then transformed before being loaded on the other end. While this process was more expensive and time-consuming, it created higher-quality data for the query.

However, the development of data lakes meant that the process of ELT come to be in its place, which provides more cost-effective solutions with additional storage capacity and less latency.

When companies use ELT, the data doesn't need to transform before being accessed for analytics. This is also the reason ELT can transform a data lake into a data swamp.

The information that goes into and comes out of the ELT is a replica. So if unorganized or garbage data goes (which it easily can without appropriate protocols), it also comes out as a duplicate. This point is where the environment of the data lake goes downhill.

What is a Data Swamp?

Ideally, a company has a data lake allowing them to store, easily locate, and retrieve data economically. A company can use ELT to efficiently transfer and store this data, thus making business flows effective and hassle-free.

However, this scenario isn't always the case, and the intent of using a data lake is lost along with the benefits of being economical and efficient.

Many companies start with a data lake and then, in a short time, create a data swamp? Well, what's a data swamp, you may ask?

A data swamp is essential data stored without organization and precise metadata to make retrieval easy. Unfortunately, a data lake can become a wasteland of data without clear organization. In many cases, copies of data or irrelevant data are gathered and dumped into storage.

Retrieving the data and then transforming data for analytics becomes a chore. This means if certain people are looking for specific data, they won't locate it.

It happens quickly once companies convert to this storage method using ELT but do not clearly outline how to use it and what the outcome should be for it. 

How ELT Creates Data Swamps

Previously, when ETL was in place, the data was high-quality and organized or deleted, or fixed. So, the environment stayed organized and ideal without considering additional protocols to ensure it stayed suitable for data storage needs.

With the integration of ELT, company data goes in and comes out as a mirror image. It doesn't need to be high quality to go in or come out because it isn't transformed before query execution. It's the main reason a company can quickly go from having a wonderfully functioning data lake to a data swamp.

It's surprising how fast this can occur. When too many individuals with access and no system of guidance or governance begin uploading data into the data lake, it can quickly become bogged down with useless, unidentifiable data. 

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What Are Data Lakes, and How do They Function?

Data lakes are a step up from older storage methods like the traditional data warehouse, but they create their own set of issues and potential pitfalls.

Data lakes allow for reduced data latency instead of the traditional data warehouse approach, which is one reason they are attractive to companies today.

However, because of its capacity for storage and no transformation of that data, it can be a dumping ground for low-quality or disorganized data. For a data lake to function properly, it needs a few elements. 

  • Metadata — brief information that accurately describes data and allows for a better storage structure. 
  • Relevant data — A data lake will only store relevant information and have limits on stored data. There must be a policy for how and why data is stored to prevent unnecessary dumping, thus preventing data gathering with no purpose. 
  • Data Governance — Data needs to be treated in a particular manner but to ensure it is, some guidance needs to be implemented. Some data protocols or governance can consist of providing specific access to people within the organization. Keep in mind that regulations in place require information within regulations set by the government. 
  • Automation — This is a must today to keep a data lake efficient. It can help regulate and standardize data use for employees. 

A takeaway from this information is that there's not a single solution that's best for every company. Some companies benefit from data warehouses and ETL, while others find greater use for data lakes and ELT. The key is to know which provides the best features for particular needs — keeping in mind the issues that may come with either option.

Summary

A data lake is an ideal environment for today's companies to store data economically when they're managed correctly. However, to maintain a data lake, specific governance and protocols must be in place to ensure data is correctly stored and retrievable.

The effectiveness and benefits of a data lake and use of ELT must have supervision and monitoring to ensure this process is effective and governance is in place.

Data Swamps can happen quickly and can create a serious problem for companies relying on this storage, extraction, and transformation system for analytics.

To prevent this from happening, working with a top cloud-based ETL and ELT solutions company with the tools, know-how, and experience to keep them on track and ensure the dreaded data swamp doesn't happen.

How Xplenty can Help

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Xplenty is a company offering cloud-based ETL solutions. They can provide automated data flows and data pipelines to accommodate various needs for today's modern companies. In addition, they can move and transform data between different data stores.

They also efficiently manage ELT and help provide the ideal system for data storage and extraction to help prevent their clients' data lakes from becoming data swamps.

Xplenty offers an automated ETL platform to make the process of data integration and transformation easier and streamlined. There are various products and services available to customize solutions for each client to ensure they get the best services and easily manage data and data storage needs. 

When companies use Xplenty, they get flexibility and convenience without struggling with maintenance and visibility issues. Additionally, it allows their clients to efficiently integrate, process, and prepare data, which can then used for analytics while using their cloud-based system.

To learn more about how Xplenty can help turn a company's data processing and storage into an effective solution, contact them today and get started with top data management services. Be sure to take advantage of a 14-day demo to see how they can improve your current data system.