What Is Data Loading?
What Is Data Loading?
We take a look at how data loading can help data teams speed up their times to insights, improve the accuracy of their data, and more.
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One of the most important aspects of data analytics is that data is collected and made accessible to the user. Depending on which data loading method you choose, you can significantly speed up time to insights and improve overall data accuracy, especially as it comes from more sources and in different formats. ETL (Extract, Transform, Load) is an efficient and effective way of gathering data from across an organization and preparing it for analysis.
Data Loading Defined
Data loading refers to the "load" component of ETL. After data is retrieved and combined from multiple sources (extracted), cleaned and formatted (transformed), it is then loaded into a storage system, such as a cloud data warehouse.
ETL aids in the data integration process that standardizes diverse and disparate data types to make it available for querying, manipulation, or reporting for many different individuals and teams. Because today’s organizations are increasingly reliant upon their own data to make smarter, faster business decisions, ETL needs to be scalable and streamlined to provide the most benefit.
Benefits of Data Loading
Before ETL evolved into its current state, organizations had to load data manually or else use several different ETL vendors for each different database or source. Understandably, this made the process slower and more complicated than it needed to be — reinforcing data silos rather than breaking them down.
Today, the ETL process — including data loading — is designed for speed, efficiency, and flexibility. But more importantly, it can scale to meet the growing data demands of most enterprises. ETL easily accommodates proliferation of data sources as technologies like IoT and connected devices continue to gain popularity. And it can handle any number of data types and formats, whether structured, semi-structured, or unstructured.
Challenges With Data Loading
Many ETL solutions are cloud-based, which accounts for their speed and scalability. But large enterprises with traditional, on-premise infrastructure, and data management processes often use custom built scripts to collect and load their own data into storage systems through customized configurations. This can:
- Slow down analysis. Each time a data source is added or changed, the system has to be reconfigured, which takes time and hampers the ability to make quick decisions.
- Increase the likelihood of errors. Changes and reconfigurations open up the door for human error, duplicate or missing data, and other problems.
- Require specialized knowledge. In-house IT teams often lack the skill (and bandwidth) needed to code and monitor ETL functions themselves.
- Require costly equipment. In addition to investment in the right human resources, organizations have to purchase, house, and maintain hardware and other equipment to run the process on site.
Methods for Data Loading
Since data loading is part of the larger ETL process, organizations need a proper understanding of the types of ETL tools and methods available, and which one(s) work best for their needs, budget, and structure.
Cloud-based. ETL tools in the cloud are built for speed and scalability, and often enable real-time data processing. They also include the ready-made infrastructure and expertise of the vendor, who can advise on best practices for each organization’s unique setup and needs.
Batch processing. ETL tools that work off batch processing move data at the same scheduled time every day or week. It works best for large volumes of data and for organizations that don’t necessarily need real-time access to their data.
Open source. Many open-source ETL tools are quite cost-effective as their code base is publicly accessible, modifiable, and shareable. While a good alternative to commercial solutions, these tools can still require some customization or hand-coding.
Published at DZone with permission of Garrett Alley , DZone MVB. See the original article here.
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