Chief Product Officer, Ispirer Systems
As the amount of data, data sources and types of data in organizations increases, so does the importance of using this data for analytics, data science and machine learning initiatives to gain business insights. Data engineering teams feel a lot of pressure when they have to prioritize these processes. The most important stage is turning messy data into clean and organized data to achieve goals.
ETL just helps at this point, as it involves extracting data from different sources, transforming them and loading them into one data warehouse. Explore the topic, understand when ETL is used, and learn how Ispirer Toolkit aids in ETL processes.
What is ETL?
ETL means getting data from different sources, changing it to a useful format, and putting it into a database. ETL tools can simplify data management and improve data quality by standardizing intake, sharing, and storage if implemented correctly.
Data-driven organizations often use ETL tools. CRM platforms have the advantage of conducting all business activities through one interface. Teams can share CRM data to gain a better understanding of business performance and progress towards goals.
How does ETL work?
Extract
During this phase, data is pulled from the original system and placed into a staging area. Any necessary transformations are performed in the staging area to avoid negatively impacting the performance of the source system.
Transferring corrupted data from source to database makes rollback hard if any data is corrupted. The staging area provides a space to verify the extracted data before it is transferred to the Data warehouse.
As a general rule, there is usually an intermediary layer between the source and the destination where transformations on the extracted data are carried out. There are 3 ways of extracting data:
- Partial extraction (with alert). Pull data that comes with an update alert from the source. If there are changes to previously extracted data, we only pull the altered data into the staging area.
- Partial Extraction (without update alert). Pull data that has specific loading conditions, like daily schedulers that only extract the data for that day. Any new data added will be extracted on the corresponding day.
- Full Extraction. In this method, data is completely extracted from the source system. The source data will be provided as is and no additional logical information is necessary on the source system. Since it is a complete extraction, there is no need to track the source system for any changes.
Transform
The subsequent phase involves converting the unprocessed data, which has been pulled from various sources, into a structure that is compatible with diverse applications. During this stage, the data undergoes cleaning, mapping, and conversion, typically to a particular schema, to fulfill operational requirements. This procedure encompasses various transformation types that guarantee the data's quality and reliability. Basic transformation techniques are the following:
- Format Standardization: Standardize the data type and length according to field format to make it easy for the end user to retrieve data.
- Cleaning: Includes mapping of values to some derived/short meaning like mapping ‘Male’ to ‘M’, null to ‘0’, etc.
- Deduplication: Involves removing of duplicate values.
- Constraints Implementation: Establishment of key relationships across tables.
Load
The final phase involves seamlessly transferring the data to its new destination, namely the Warehouse. This is an intricate step that demands meticulous precision. Handling vast amounts of data necessitates a well-thought-out loading strategy tailored to the specific data type. Consequently, optimizing this process becomes imperative for the efficient execution of operations. Below, we elucidate two fundamental approaches for the Load process.
- Full Loading. Within the ETL framework, Full Load entails transferring all data from the source to the destination. Before the transfer, the target table undergoes truncation, rendering this technique as Destructive Load. While it represents a straightforward method for moving data from source to destination, it consumes time and effort, making it less ideal as a default implementation strategy.
- Incremental Loading. When dealing with extensive data that cannot be loaded in a single operation, or when the majority of records remain unchanged amidst substantial data volume, Incremental Load is employed. This method involves recording batches of data, ensuring no data is overlooked, and executing the process seamlessly. Moreover, Incremental Load can be further categorized based on the increment type, such as Batch Increment Load and Streaming Increment Load.
How ETL is being used?
Understanding when to employ ETL migration is a crucial aspect of its proper usage. Here are a few instances and scenarios where ETL migration is most effectively used:
- ETL in data warehousing. ETL's most prevalent application is in data warehousing. For instance, when a client wants to incorporate historical data into their existing warehouse, ETL tools consolidate this data, offering users straightforward access to vital insights.
- Facilitating self-service reporting. ETL tools remove the requirement for IT staff or technical assistance in generating data reports. These solutions can effortlessly extract data from throughout the organization and produce data visualizations like graphs, charts, maps, or dashboards. By making data accessible to everyone, all employees can access and analyze data for decision-making.
- Offers real-time monitoring and alerts. ETL instruments can establish conduits that incessantly pull data from diverse sources and manipulate it instantaneously. Through real-time evaluation, enterprises can dynamically observe and scrutinize crucial indicators as they occur, which propels business results.
Ispirer Toolkit as an ETL tool
Ispirer Toolkit - a tool for migrating databases and applications can be used as an ETL tool in different cases. First of all, the tool allows you to upload data in the usual one-to-one mode, that is, one source table is converted into one target table. This mode provides the following features:
- Replacement of data types, if there is such a need;
- Changing the data format, if you need to change, for example, the date format, the length of a string, or change the dimension of a number;
- Changing the names of objects: schemas, tables, columns;
- Changing the structure of objects - you can selectively specify the columns necessary for conversion in the table so that the output is a table with a different structure.
- Apply a filter to the selected data
Ispirer Toolkit also makes it possible to upload objects in Use Query mode. A user can write a query using the SQL features of the source database. In such a case, the result is uploaded to the target database. This method requires special expertise, but offers unlimited possibilities.
The toolkit has a convenient GUI in the form of SQLWays Wizard. The user can quickly and conveniently configure the task parameters (what should be overloaded and where). The tool also has command line support. This makes it easy to schedule the execution of the configured tasks. In addition, if required, you can write a script that changes the task parameters at startup.
Let us automate your ETL process
Ispirer Toolkit offers a set of features that make it stand out from other ETL tools. The product is perfect for businesses. It has an easy-to-use design, strong features, and a successful history.
It is a powerful and user-friendly option. Ispirer Toolkit assists organizations in optimizing their data resources. It achieves this by managing various data sources, handling complex transformations, and improving the efficiency of the ETL process.
To learn more about how our tool can help you automate the ETL process, contact us.