The ETL process seems quite straight forward. As with every application, there is a possibility that the ETL process fails. This can be caused by missing extracts from one of the systems, missing values in one of the reference tables, or simply a connection or power outage. Therefore, it is necessary to design the ETL process keeping fail-recovery in mind.
It should be possible to restart, at least, some of the phases independently from the others. For example, if the transformation step fails, it should not be necessary to restart the Extract step. We can ensure this by implementing proper staging. Staging means that the data is simply dumped to the location called the Staging Area so that it can then be read by the next processing phase.
The staging area is also used during ETL process to store intermediate results of processing. This is ok for the ETL process which uses for this purpose. However, tThe staging area should is be accessed by the load ETL process only. It should never be available to anyone else; particularly not to end users as it is not intended for data presentation to the end-user.
When you are about to use an ETL tool, there is a fundamental decision to be made: will the company build its own data transformation tool or will it use an existing tool? Building your own data transformation tool usually a set of shell scripts is the preferred approach for a small number of data sources which reside in storage of the same type. The reason for that is the effort to implement the necessary transformation is little due to similar data structure and common system architecture.
Also, this approach saves licensing cost and there is no need to train the staff in a new tool. This approach, however, is dangerous from the TOC point of view.
If the transformations become more sophisticated during the time or there is a need to integrate other systems, the complexity of such an ETL system grows but the manageability drops significantly.
Similarly, the implementation of your own tool often resembles re-inventing the wheel. There are many ready-to-use ETL tools on the market. The main benefit of using off-the-shelf ETL tools is the fact that they are optimized for the ETL process by providing connectors to common data sources like databases, flat files, mainframe systems, xml, etc.
They provide a means to implement data transformations easily and consistently across various data sources. This includes filtering, reformatting, sorting, joining, merging, aggregation and other operations ready to use. The tools also support transformation scheduling, version control, monitoring and unified metadata management. Informatica offers real-time data integration, Web services integration, Business to business data integration B2B , Big data edition, Master Data Management and connectors for social media and Salesforce.
Forbes has quoted Informatica as the next Microsoft, this itself reflects the market share Informatica is having over its competitors. Informatica comes to the picture wherever we have a data system available and at the backend we want to perform certain operations on the data. It can be like cleaning up of data, modifying the data, etc. Informatica software offers a rich set of features like operations at row level on data, integration of data from multiple structured, semi-structured or unstructured systems, scheduling of data operation.
It also has the feature of metadata, so the information about the process and data operations are also preserved. Skip to content. What is Informatica? An organization migrating from existing legacy system like mainframe to a new database system. So the migration of its existing data into a system can be performed. Integration of data from various heterogeneous systems like multiple databases and file-based systems can be done using Informatica.
ETL is an essential component of data warehousing and analytics, but not all ETL software tools are created equal. The best ETL tool may vary depending on your situation and use cases. Here are 7 of the best ETL software tools for , along with a few others that you may want to consider:. Integrate your Data Warehouse today Turn your data warehouse into a data platform that powers all company decision making and operational systems.
The Integrate. More than popular data stores and SaaS applications are packaged with Integrate. Scalability, security, and excellent customer support are a few more advantages of Integrate. For example, Integrate. Thanks to these advantages, Integrate.
Support and development have been very responsive and effective. The Talend platform is compatible with data sources both on-premises and in the cloud, and includes hundreds of pre-built integrations. The paid version of Talend includes additional tools and features for design, productivity, management, monitoring, and data governance. Talend has received an average rating of 4. Reviewer Jan L. FlyData is a cloud-based real-time data integration platform.
FlyData supports the replication of data from numerous sources into Amazon Redshift, Snowflake and S3. FlyData has a clear product differentiation in the time it takes to set up data replication and the speed at which it is able to perform the replication of numerous rows of data.
FlyData is highly recommended for any company that values speed and reliability in data integration, such as e-commerce. FlyData has a rating of 4. Reviewer Priyam J. This enabled Eight to share KPI reports across the team every morning at a set time.
Informatica PowerCenter is a mature, feature-rich enterprise data integration platform for ETL workloads. PowerCenter is just one tool in the Informatica suite of cloud data management tools. As an enterprise-class, database-neutral solution, PowerCenter has a reputation for high performance and compatibility with many different data sources, including both SQL and non-SQL databases.
Despite these drawbacks, Informatica PowerCenter has earned a loyal following, with an average of 4. Reviewer Victor C.
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