An overview of the
Taskctl is an open source ETL tool written in C and can run on Windows, Linux, and Unix.
To put it bluntly, it is important to understand the features and functions required by common ETL tools in order to better understand taskCTL.
Today I will begin by describing the general functionality of the ETL tool.
One of the functions of the ETL tool: connect
Any ETL tool should have the ability to connect to a wide variety of data sources and data formats. For the most common relational database systems, which also provide a local connection (such as OCI for Oracle), ETL should provide the following basic functions:
- Connect to and retrieve data from common relational databases such as Orcal, MS SQL Server, IBM DB/2, Ingres, MySQL, and PostgreSQL. And on and on
- Gets data from delimited and fixed format ASCII files
- Get data from an XML file
- Get data from popular office software such as Access databases and Excel spreadsheets
- Obtain data using FTP, SFTP, and SSH (no script is preferred)
- You can also get data from Web Services or RSS. If you also need data from ERP systems such as Oracle e-Business Suite, SAP/R3, PeopleSoft, or JD/Edwards, the ETL tool should also provide connectivity to these systems.
- Input steps for http://www.taskctl.com and SAP/R3 are also available, but are not in the kit and require additional installation. Other solutions are needed for data extraction for other ERP and financial systems. Of course, the most common approach is to require these systems to export data in text format as the data source.
The second feature of ETL tools is platform independence
An ETL tool should run on any platform or even a combination of different platforms. A 32-bit operating system may work well in the initial stages of development, but as the volume of data increases, a more powerful operating system is required. On the other hand, development is typically run on Windows or Mac machines. Production environments are typically Linux systems or clusters, and your ETL solution should switch seamlessly between these systems.
The third function of the ETL tool: data scale
Generally ETL can process big data in the following three ways.
- Concurrency: ETL processes can process multiple data streams simultaneously to take advantage of modern multicore hardware architectures.
- Partitioning: ETL has the ability to distribute data to concurrent data streams using a specific partitioning scheme.
- Clustering: ETL processes can be distributed across multiple machines for joint completion.
Kettle is a Java-based solution that can run on any computer (including Windows, Linux, and Mac) that has Java VIRTUAL Machines installed. Each step in the transformation is executed concurrently and multiple times, which speeds up processing.
During a Kettle conversion process, data can be sent to multiple data flows in different modes (distribution and replication) based on user Settings. Distribution is similar to card distribution. Each row of data is sent to only one data stream in turn. Replication is to send each row of data to all data streams.
To control data more precisely, Kettle uses a partitioning mode to send data of the same characteristics to the same data flow. The partitions here are only conceptually similar to database partitions.
Kettle does not have any functionality for database partitions.
Function number four of ETL tools: Design flexibility
An ETL tool should leave developers with enough freedom to use it without limiting users’ creativity and design needs in a fixed way. ETL tools can be divided into process-based and mapping-based.
Mapping-based functionality provides only a fixed set of steps between source and destination data, severely limiting the freedom of design effort. Mapping-based tools are generally easy to use and quick to pick up, but for more complex tasks, process-based tools are the best assembled choice.
Using a process-based tool like Kettle, you can create custom steps and transformations based on actual data and potential requirements.
Function number five of ETL tools: reusability
It is important that the ETL transformations are designed to be reusable. Copying and pasting existing transformation steps is the most common type of reuse, but it is not really reuse.
Taskctl has a mapping (subtransformation) step that can reuse a transformation as a subtransformation of another transformation. In addition, transformations can be used multiple times in multiple jobs, which can also be subjobs of other jobs.
The sixth feature of the ETL tool: extensibility
As you know, almost all ETL tools provide scripts that programmatically solve problems that the tools themselves cannot solve. In addition, there are a few ETL tools that can add components to tools through apis or other means. Write functions in a scripting language that can be called by other transformations or scripts.
Kettle provides all of the above functionality. A Java script step can be used to develop a Java script, save the script as a transformation, and map (subtransformation) steps into a standard reusable function. In fact, not limited to scripts, every transformation can be reused in this mapping (subtransformation) manner, as if creating a component. Kettle is extensible by design and provides a plug-in platform. This plug-in architecture allows third parties to develop plug-ins for the Kettle platform.
All the plugins in the Kettle, even the components provided by default, are actually plugins. The only difference between built-in third party plugins and Pentaho plugins is technical support. Suppose you buy a third-party plug-in (such as a connection to SugarCRM) and the technical support is provided by the third party, not Pentaho.
ETL tool function 7: data conversion
A large part of the ETL project is data transformation. Between inputs and outputs, data is checked, joined, separated, merged, transposed, sorted, merged, cloned, resorted, filtered, deleted, replaced, or otherwise.
Data transformation requirements vary widely across organizations, projects, and solutions, so it is difficult to say what the minimum transformation capabilities should be provided by an ETL tool.
However, common ETL tools (including Taskctl) provide the following basic integration capabilities:
- Change dimensions slowly
- The query value
- The ranks of conversion
- Conditions for separation
- Sort, merge, join
- gather
ETL tool function 8: test and debug
Testing is usually divided into black box testing (also known as functional testing) and white box testing (structural testing).
Black box testing, ETL transformation is considered a black box, the tester does not know the function of the black box, only the input and expected output.
White-box testing, which requires testers to know the inner workings of transformations and to design test cases accordingly to check whether a particular transformation has a particular result.
Debugging is actually a part of white-box testing, with a height that allows developers or testers to run a transformation step by step and figure out where the problems are.
ETL tool function nine: ancestry analysis and impact analysis
One important function that any ETL tool should have is to read the metadata of the transformation, which is to extract information about the data flow made up of the different transformations.
Pedigree analysis and impact analysis are based on two related characteristics of metadata.
Lineage is a retrospective mechanism to see where the data came from.
Impact analysis is another meta-data-based analysis method that analyzes the impact of source data on subsequent transformations as well as the target tables.
ETL tool # 10: Logging and auditing
The purpose of a data warehouse is to provide an accurate information source, so the data in a data warehouse should be reliable and trusted. To ensure that this matrix is reliable and that all data conversion operations can be recorded, ETL tools should provide logging and auditing capabilities.
The log records which steps are performed during the transformation, including the start and end timestamps of each step.
Auditing can track all operations done to the data, including the number of rows read, converted, and written.
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