Data Loading is defined as copying data from one electronic file or database into another. Data loading implies converting from one format into another; for example, from one type of production database into a decision support database from a different vendor.
This article will give a Comprehensive Guide on Data Loading
Table of Contents
Introduction to Data Loading
Data loading defines the LOAD component of the ETL process. ETL stands for Extraction, Transformation, and Load. Extraction deals with the retrieval and combining of data from multiple sources. Transformation deals with cleaning and formatting of the Extracted Data. Data Loading deals with data getting loaded into a storage system, such as a cloud data warehouse.
ETL aids in the data integration process that standardizes diverse data types to make them available for querying, manipulation, or reporting for many different individuals and teams. Because today’s organizations are increasingly dependent upon their own data to make smarter, faster business decisions, ETL needs to be scalable and streamlined to provide the most benefit.
Data loading is quite simply the process of packing up your data and moving it to a designated data warehouse. It is at the beginning of this transitory phase where you can begin planning a roadmap, outlining where you would like to move forward with your data and how you would like to use it.
Data Loading is the ultimate step in the ETL process. In this step, the extracted data and the transformed data are loaded into the target database. To make the data loading efficient, it is necessary to index the database and disable the constraints before loading the data. All three steps in the ETL process can be run parallel. Data extraction takes time and therefore the second phase of the transformation process is executed simultaneously. This prepares the data for the third stage that is data loading. As soon as some data is ready, data loading is done without waiting for the previous steps to be completed.
Hevo Data, a No-code Data Pipeline helps to load data from any data source such as Google Search Console, Databases, SaaS applications, Cloud Storage, SDKs, and Streaming Services and simplifies the ETL process. It supports 100+ data sources (including 30+ free data sources) and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo not only loads the data onto the desired Data Warehouse/destination but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.
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Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different BI tools as well.
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- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
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 perform data loading on 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.
- Unorganized Data: Loading your data can become unorganized very fast. For ETL voyagers, common roadblocks that many encounters early on can be resolved with proper planning and delivery.
- Universal formatting: Before you begin loading your data, make sure that you identify where it is coming from and where you want to go.
- Loss of data: Tracking the status of all data is critical for a smooth loading process.
- Speed: Although it’s exciting to be closer to your final destination, do not rush through this phase. Errors are most likely to occur during this time.
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.
In the process of Data Loading the data is physically moved to the data warehouse. The Data Loading takes place within a “load window. The tendency is close to real-time updates for data warehouses as warehouses are growing used for operational applications.
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 codebase is publicly accessible, modifiable, and shareable. While a good alternative to commercial solutions, these tools can still require some customization or hand-coding.
Types of Data Loading
Soon after your departure from the extraction phase, you will be faced with the decision of which loading process that you would like to deploy. The data loading process is the physical movement of the data from the computer systems storing the source database(s) to that which will store the data warehouse database. The entire process of transferring data to a data warehouse repository is referred to in the following ways:
- Full Load: This is where all of your data is selected, moved in bulk, and then replaced by new data. Although it is not as complex to navigate through, loading time is much slower. With the overwhelming amount of data being moved at once, it is much easier for data to get lost within the big move.
- Incremental Load: This is where you are moving new data in intervals. Due to its intricate nature, delivery time is much faster than its counterpart. However, this speed comes at a cost. Incremental loads are more likely to encounter problems due to the nature of having to manage them as individual batches rather than one big group. Incremental Load Periodically applies ongoing changes as per the requirement. After the data is loaded into the data warehouse database, verify the referential integrity between the dimensions and the fact tables to ensure that all records belong to the appropriate records in the other tables. The DBA must verify that each record in the fact table is related to one record in each dimension table that will be used in combination with that fact table.
- Initial Load: For the very first time loading all the data warehouse tables.
- Full Refresh: Deleting the contents of a table and reloading it with fresh data.
Data Loading: Refresh versus Update
After the initial load, the data warehouse needs to be maintained and updated and this can be done by the following two methods:
- Update-application of incremental changes in the data sources.
- Refresh-complete reloads at specified intervals.
In the present-day market, ETL equipment is of great value, and it is very important to recognize the classified method of extraction, transformation, and data loading method.
1) Hevo Data
Hevo Data, a No-code Data Pipeline, helps to transfer data from 100+ sources to your desired data warehouse/ destination and visualize it in a BI tool. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
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Hevo Data Use Case
Hevo provides a seamless data pipeline experience to companies. Hevo supports pre-built integration with 100+ data sources and allows data migration in real-time. With its ETL, ELT and data transformation capabilities, you will always have analysis-ready data.
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Skyvia is one of the most popular Cloud ETL Tools that provide users with robust data integration, migration and backup support. Being a SaaS application, it only requires users to have smooth internet connectivity and a web browser to be able to access it.
Skyvia’s impeccable no-code data integration wizard allows users to bring in data from a variety of sources such as databases, cloud applications, CSV files, etc. to data warehouses of their choice such as Google BigQuery, Amazon Redshift, etc.
Some of the common issues that you might encounter while using Skyvia is that it doesn’t have fast customer support response times. Similarly, another problem with Skyvia is that it provides less integration support and transformation functionalities.
Skyvia Use Case
Skyvia can be a suitable choice for you if you’re looking for a tool that provides a no-code solution to help you automate your ETL pipelines, and you’re okay with minimal data transformation functionalities.
For further information on Skyvia, you can check the official website here.
Xplenty is a robust Cloud ETL Tool that provides an easy-to-use data integration platform and helps you integrate data from a diverse set of sources. Its intuitive user interface lets users set up data pipelines with ease.
It houses powerful data transformation functionalities that allow users to clean, transform and normalise their data into an analysis-ready form. It provides integration support with a diverse set of sources such as on-premise databases, cloud applications, SaaS offerings, etc. such as MongoDB, MySQL, PostgreSQL, etc.
Xplenty Use Case
Xplenty can prove to be the right choice for companies that want an easy-to-use no-code data integration platform to manage their ELT and ETL workloads. It can be a good choice for businesses that don’t want to invest much in their engineering bandwidth and prefer leveraging pre-built integrations and functionalities such as drag and drop features.
For further information on Xplenty, you can check the official website here.
Talend is an open-source Cloud ETL Tool that provides more than 100 pre-built integrations and helps users bring in data from both on-premise and cloud-based applications and store it in the destination of their choice.
With Talend, you can seamlessly work with complex process workflows by making use of the large suite of apps provided by Talend. You can manage the design, testing and deployment of your integrations. It also provides a smooth drag and drops functionality along with an open studio feature for beginners.
Talend Use Case
Talend is a suitable choice for companies that require the flexibility of a diverse set of pre-built integrations and are looking for an open-source ETL solution.
For further information on Talend, you can check the official website here.
5) Informatica PowerCenter
Informatica PowerCenter is an enterprise-grade data integration platform. It is one of the most robust and well-reputed Cloud ETL Tools in the market and is available as one of the tools in the Informatica cloud data management suite.
It performs exceptionally well and helps integrate data from numerous data sources, including various SQL and NoSQL databases. PowerCenter’s data integration platform is highly scalable, and scales as your business grows to manage your business and data needs and helps transform fragmented data into an analysis-ready form.
Some of the common issues you might face using Informatica is that it has a steep learning curve and requires users some time to learn and understand the platform. Similarly, it can turn out to be an expensive solution for various small businesses.
Informatica PowerCenter Use Case
If your company is a large enterprise that can support expensive ETL solutions and has a challenging workload that requires high-end performance, then Informatica can be the right choice. You must also be ready to invest a large amount of time in learning the platform as it has a steep learning curve.
For further information on Informatica, you can check the official website here.
Fivetran is a cloud-based ETL tool that delivers high-end performance and provides one of the most versatile integration support, supporting over 90+ SaaS sources apart from various databases and other custom integrations.
It is fully managed and helps deploy automated ETL pipelines in a matter of minutes. It has an easy-to-use platform with a minimal learning curve that allows you to integrate and load data to various data-warehouses such as Google BigQuery, Amazon Redshift, etc. It further adapts to changes in the API and schema easily.
One of the common issues that you might face while using Fivetran is that if there’s an error or technical issue, it becomes challenging to figure out the cause of it. Further, Fivetran customer support tends to be slow in responding to your queries.
Fivetran Use Case
Fivetran is a suitable choice for companies that require the flexibility of a diverse set of pre-built integrations.
For further information on Fivetran, you can check the official website here.
7) Stitch Data
Stitch Data is an open-source cloud-based ETL tool that is suitable for businesses of all kinds, even large enterprises. It provides users with intuitive self-service ELT pipelines that are fully-automated, allowing users to integrate data from various data sources such as SaaS applications, databases and store it in data warehouses, data lakes, etc.
Stitch doesn’t support much transformation functionalities and requires users to load the data and then transform it. It provides more advanced features to users as they go higher in the pricing tiers.
One common issue that most Stitch users face is the lack of support for some data sources and minor technical errors that occur frequently. Although Stitch has an easy-to-use UI, it can take some time to adjust to the UI.
Stitch Use Case
Stitch is suitable for companies that are looking for an open-source tool that provides a no-code solution to help them automate their ETL pipelines, and are okay with having minimal data transformation functionalities.
For further information on Stitch, you can check the official website here.
This article gives a comprehensive overview of the Data Loading component of the ETL process. It also gave loads of tools that are cloud-based and can ease the process of ETL.
To make things easier, Hevo comes into the picture. Hevo Data is a No-code Data Pipeline and has awesome 100+ pre-built Integrations that you can choose from.
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Hevo can help you Integrate your data from numerous sources and load them into a destination to Analyze real-time data with a BI tool such as Tableau. It will make your life easier and data migration hassle-free. It is user-friendly, reliable, and secure.
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