One such example is the use of SQL-like scripts to perform ETL tasks, only to realize that SQL primitives do not support one-to-many mappings one transformation produces many output tuples , complex transformations which require coding in Java, and other real-world transformations resulting in spaghetti code where callouts to user-defined functions UDF are interspersed with Java code, making the code unmonitorable and unsupportable.
Some examples of data-transformation functions include aggregations count, average , filtering, various kinds of joins in-memory lookup, large data sets on either side , merging, and custom functions masking.
What is required is a framework that provides commonly used data-transformation functions out-of-the-box, and then makes it simple to extend and add custom transformation functions. Ontology represents knowledge within a domain. Any real-world ETL workflow must have multiple steps in producing a data product. Historically, creating a data application without developing a model to capture the flow from source to the final product will result in a system that becomes unmanageable and unsupportable.
In other words, what is required is an overall architecture graph that is a logical-level description of the data flow in an ETL process. Each node of such a graph represents activities and the affected attributes. Figure 1: A simple ontology of a word count application rendered through Driven. You can explore this particular application by clicking on this link. ETL workflow instances or data applications rarely exist in isolation. The output of one data flow is typically the source for another data flow.
In a real-world ETL deployment, there are many requirements that arise as a result. First, the ETL framework must be able to automatically determine dependencies between the flows. Many orchestration frameworks exist, but are hampered from deployment due to their complexity of use; ease of use — and ideally, the ability to automatically sense dependencies is a vital requirement. Second, to optimize the compute resources of the Hadoop cluster, the system must be intelligent to only execute downstream data flows if the source data has been updated otherwise, the downstream flow will execute to create an identical result set from its previous run, wasting resources.
Third, in case of an error in a system with many dependent flows, the framework should be intelligent enough not to start from the beginning, but from the last successfully-completed flow in the dependency graph checkpoints.
Yet, little attention is initially paid to supporting operational visibility required to support the deployment and monitoring the ability of the ETL flow to meet its SLA. What is required in the ETL framework is the ability to visualize application tuning, monitoring, trending for capacity and compute use, and getting insights into when and how the applications miss their SLAs. One of its salient features is that it has a very small memory footprint and Easy Batch has no dependencies.
Easy Batch can be ran in two ways. It can either be embedded into an application server or it can run as a standalone application. Apache Camel is an open source integration framework in Java that can be used to exchange, transform and route data among applications with different protocols. With Apache Camel, you have the ability to make your routing rules, determine the sources from which to accept messages and decide on how to process and send those messages to other components of the application.
Apache Nifi is an open source stream processing tool. It allows for data to be published as well as subscribed from a range of streaming sources. The streamed data can then be made to go through a series of processing options aimed at inferring and extracting information from the data. Like its competition, Nifi provides the ability to interact with clusters, implement distributed processing, secure data communication over SSL, ensure minimum response time and offer fail-safe reliability.
To hide the complexity of stream processing tasks, Nifi provides a web-based graphical user interface which automates the configuration work required for processing streams. A sample GUI in action can be found here: Summing up, Nifi allows Spark to complement its batch processing capability by offering a versatile, open-source stream processing framework. The tool allows for a combination of relational and non-relational data sources. It also includes a business modeler for a non-technical view of the information workflow and a job designer for displaying and editing ETL steps.
A debugger also exists for real-time debugging. Apatar is an open source ETL based on Java. Its feature set include single-interface project integration, visual job designer for non-developers, bi-directional integration, platform independence and the ability to work with a wide range of applications and data sources such as Oracle, MS SQL and JDBC.
These features not only make it a rival to competing commercial solutions but also make the ETL highly extensible. Apache Spark provides stream as well as batch processing. This library acts as a fault tolerant framework that can process real time data at high throughputs. Data is dealt in streams and can be read from a wide variety of sources including Kafka, Twitter and Zeromq. In addition, custom streaming sources can also be defined for wider data coverage.
The Spark engine processes data by dividing it into a series of successive batches, with each batch corresponding to a different processing stage. Talend is the first data integration product, and it was launched in It supports data migration, profiling, and warehouse.
Talend data integration platform supports data monitoring and integration. It also provides services like data management, data preparation, data integration, etc. Following are the important features of Talend:. Note: We can use the Talend tool freely for 14 days Free Trial , after that, we can buy it according to our requirement. Stitch is the first cloud-based open-source platform that enables users to move data rapidly.
It is an easy and expandable ETL tool that is built for the data groups. Note: We can use the Stitch ETL tool freely for 14 days, after that, we can buy it based on our requirement.
Pentaho kettle is the element of Pentaho, and it is useful to extract, transform and load the data. We can use the Kettle tool to migrate the data between the databases or applications. Through this tool, we can load the data into the databases. Panoply builds and manages cloud data warehouses for you.
You can try it for free or get a personalized demo. With JasperSoft, you can create dynamic BI content for websites and apps as well as print-quality files. So, we grouped this ETL tool in the paid section of the blog. Cascading in an open source API created for Java developers and engineers. With Cascading, you can build complex apps and perform high-level data operations that require coding in Java. It uses pipelines and filters to stream and transform data from its source to the data warehouse.
Or else download one of its stable releases. To run more complex data transformations from your existing Java code libraries, you can extend CloverDX with your own custom Java functions. CloverDX has a code debugger, and this ETL tool lets you write hackable code and generate code transformations. With its open architecture, developers can collaborate and integrate transformations in a DevOps-style workspace.
Topics Topics. Speak with a Panoply Data Architect. Resources Resources. Integrations Integrations. Why Panoply. Demo Demo. Visit Panoply. By Trisha McNary May 14, Free and open source Java ETLs 1. Apache Camel Apache Camel is an open source Java framework that integrates different apps by using multiple protocols and technologies.
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