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What is ETL testing?
ETL (Extract, Transform and Load) testing is the process of extracting data from the source and transforming, validating, verifying and qualifying data in order to ensure data integrity and quality.
It involves identifying different business requirement so as to make a methodology that is tailor-made to the client. It closely required validation of the different sources of data which can then lead to the most significant step which is designing test cases and creating scripts in SQL. This helps in further applying transformation logic and making sure that the data type that is changes matches the initially mapped document for every word and letter. The final steps of ETL testing includes executing the schema and bug reporting to make certain that the summary report that is produced at the end is accurate.
On that front, it is also essential that one makes a record count prior to and after data is moved from the staging warehouse to the target data warehouse. This makes sure that there is no loss of data of replicas of information filed. The following are some of the drawbacks of using the older and more traditional methods of ETL testing for load testing services.
Drawbacks of Traditional Methods
- Traditionally taken forward the process of ETL testing includes a tedious testing of different sources and target data to ensure that the data is recent.
- This process can be extremely time consuming when there are many sources and high volumes of data for some projects that can have over thousands of sources.
- This makes it so that is it also error prone as a process which would require repeated interventions to make certain that there are quality checks.
- Test tools that are produced in house and maintained for further uses are always cheaper than not having an automation tool whatsoever.
Contextualizing the DevOps approach to ETL testing
The very word DevOps is a mixture of two words- development and operations. This is to imply that there is a united effort with collaborators from different application development teams as well as information technology operations groups.
DevOps emphasizes on adopting intuitive software, automated workflows, and a customizable infrastructure than can be constantly developed on. In this kind of atmosphere, communication is key, as well as keeping in mind that all members of the organization have an equal contribution to make. DevOps has some key features where are as mentioned below:
- Cloud storage and computing as a whole makes it so that operations are taken forward as soon as the DevOps methodologies are put down.
- Task automation plays a crucial role in continuously working on, deploying and delivering information. This is achieved by the use of CI/CD (Continuous integration and Continuous Delivery) tools.
The whole methodology of DevOps is done throughout the lifecycle of the application by improving through constant feedback. There are initial steps of planning which leads to coding and building. It is then tested and deployed to monitor how it’s operated in the field and the feedback gets back to the primal steps of planning for better coding.
One of the biggest advantages of a DevOps approach is that there is little chance for glaring mistakes to occur after the release of the application. In order to build a rigid application, it’s important that coders work closely with realistic circumstances. In traditional hierarchies of application development, teams are often put in silos- which translates to developers being satisfied if the coding is merely functional. The operations team then has to work with feedback and make the necessary changes with leads to an increase in labour.
Snowflake: A Platform for Data Solutions
Snowflake provides data engineering solutions that range from loading, integrating, analysing and securely sharing one’s data. It’s flexibility and scalability are owed to its fully-managed system that is straightforward. It works with solutions for data warehousing, data engineering, application development & data science.
Some of snowflake’s solution in the industry include Financial Services, Retail, Technology, Education, Manufacturing, Healthcare Services & Media and Entertainment. In the department solutions side, Snowflake provides Marketing Analytics, Security for Data Lakes and Product Development.
What and Why use Data Cloud with Snowflake?
The fact that a single platform with one copy of data and a variety of workloads is delivered in the same pipeline is facilitated by data cloud technology. It allows structured and unstructured data from many workloads across clouds to be worked with. The scalability of Snowflake is near unlimited as it can work with concurrent workloads with no degradation in its performance.
The democratization and security of data that is achieved through the use of clouds makes sure the Snowflake empowers its users to share and consume a shared data across other units and customers. Avoiding data silos and having a integrated experience is important, in order to operate fluidly across multiple cloud service providers and platforms. The rift between data and accessing it is completely gone as data is made accessible through secure data sharing capabilities.
Conclusion
Snowflake helps you broaden and enhance your analytics by getting access to industry data sets and application through the data cloud. Actioning one’s data has never been easier as the data cloud helps executing your data workloads which use machine learning models to drive innovation and make informed business decisions according to data-driven insights.
The data cloud facility being an integral part of Snowflake’s solutions makes it so that there is a continuous quality check at every step of the application’s lifecycle. It works to develop application in the most intuitive manner where communication plays a intrinsic role in getting the operational team to adhere to the ever-changing modules that arise due to constant feedback. This DevOps approach to ETL testing might just be the solution you need.
