A database developer is responsible for: Design new databases that meet the needs of the users or customers, in an efficient and accurate format. Make modifications to the design of existing databases to meet client needs, develop the database code to perform a range of tasks, including: Extracting data from a database for analysis for reports. Creating, updating, extracting, or deleting data as required by an application. Designing and developing business intelligence reports using existing software systems that link to a database. Decide on or advise the business on database languages and technologies, ensure projects meet the standards and requirements for database design and development. Create documentation for new or existing databases to assist their team and other areas of the business. Assisting software teams with database-related activities for deploying applications, what about administration? There are other database-related areas to focus on, such as maintenance of the database, upgrades, backups, and installation.
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Table of Contents, heres what ive covered in this article. Click on each of the headings to move down the page to that section: What is a database developer? A database developer is an, it professional who is responsible for creating databases and resume database software. They design databases and work on the code that sits between an application and the data in a database. Database developers, or database programmers, often work in an it department as part of a software development team. They know specific programming languages and have certain skills that make them useful to software teams and the company as a whole. What does a database developer Do? If youve read some job descriptions for database developer jobs, you might have some idea. Or you might be more confused! So, lets clear.
Login, geekinterview, login, create your, geekinterview account. Continue reading after Disabling, refresh. So, youre best interested in becoming a database developer? Ill show you the path you can take, what skills are needed, and much more, in this article. But first, lets start with some basics. You might already know what a database developer is, but Ill explain it anyway, in case youre not sure. If you already know this, feel free to skip past this and read the steps on how to become a database developer.
He is having extensive experience in managing multi-technology qa projects, Processes and teams. Have you worked on etl testing? Please share your etl/DW testing tips and challenges first below. Data warehousing Interview questions m, showing questions 1 - 20 of 208 questions. First Prev, next, last, page, sort statement by: Answers, date. Showing questions 1 - 20 of 208 questions. Please turn off your ad blocker - OR, login to continue using geekinterview website.
Etl testing Challenges This testing is quite different from conventional testing. There are many challenges we faced while performing data warehouse testing. Here are few challenges i experienced on my project: Incompatible and duplicate data loss of data during etl process Unavailability of the inclusive testbed Testers have no privileges to execute etl jobs by their own Volume and complexity of data are very huge fault. Etl testing plays a significant role validating and ensuring that the business information is exact, consistent and reliable. Also, it minimizes the hazard of data loss in production. Hope these tips will help ensure your etl process is accurate and the data warehouse build by this is a competitive advantage for your business. Complete list of etl testing Tutorials: Tutorial 1 : etl testing Data warehouse testing Introduction guide tutorial 2 : etl testing Using Informatica powerCenter tool Tutorial 3 : etl. Db testing Tutorial 4 : Business Intelligence (BI) Testing: How to test Business Data tutorial 5 : Top 10 etl testing tools This is a guest post by vishal Chhaperia who is working in an mnc in a test management role.
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In this phase, sign off is given to promote the job or code to the next phase. The first two phases. Requirement understanding and validation can be regarded as pre-steps of etl test process. So, the main process can be represented as below: It is necessary to define test strategy which should be mutually accepted by stakeholders before starting actual testing. A well-defined test strategy will make sure that correct approach has been followed meeting the testing aspiration. Etl/Data warehouse testing might require writing sql statements extensively by testing team world or maybe tailoring the sql provided by the development team. In any case, a testing team must be aware of the results they are trying to get using those sql statements.
Difference between Database and Data warehouse testing There is a popular misunderstanding that database testing and data warehouse is similar while feux the fact is that both hold different direction in testing. Database testing is done using a smaller scale of data normally with oltp (Online transaction processing) type of databases while data warehouse testing is done with large volume with data involving olap (online analytical processing) databases. In database testing normally data is consistently injected from uniform sources while in data warehouse testing most of the data comes from different kind of data sources which are sequentially inconsistent. We generally perform the only crud (Create, read, update and delete) operation in database testing while in data warehouse testing we use read-only (Select) operation. Normalized databases are used in db testing while demoralized db is used in data warehouse testing. There is a number of universal verifications that have to be carried out for any kind of data warehouse testing. Below is the list of objects that are treated as essential for validation in this testing: Verify that data transformation from source to destination works as expected Verify that expected data is added to the target system Verify that all db fields and field data.
Are tested in this case. 10) Duplicate data Check : Test if there is any duplicate data present in the target systems. Duplicate data can lead to wrong analytical reports. Apart from the above etl testing methods other testing methods like system integration testing, user acceptance testing, incremental testing, regression testing, retesting and navigation testing is also carried out to make sure everything is smooth and reliable. Etl/ Data warehouse testing Process Similar to any other testing that lies under Independent Verification and Validation, etl also goes through the same phase. Requirement understanding Validating Test Estimation based on a number of tables, the complexity of rules, data volume and performance of a job.
Test planning based on the inputs from test estimation and business requirement. We need to identify here that what is in scope and what is out of scope. We also look out for dependencies, risks and mitigation plans in this phase. Designing test cases and test scenarios from all the available inputs. We also need to design mapping document and sql scripts. Once all the test cases are ready and are approved, testing team proceed to perform pre-execution check and test data preparation for testing Lastly, execution is performed till exit criteria are met. So, execution phase includes running etl jobs, monitoring job runs, sql script execution, defect logging, defect retesting and regression testing. Upon successful completion, a summary report is prepared and closure process is done.
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2) resumes source to target count Testing : make sure that the count of records loaded in the target is matching with the expected count. 3) source to target Data testing : make sure that all paper projected data is loaded into the data warehouse without any data loss and truncation. 4) Data quality testing : make sure that etl application appropriately rejects, replaces with default values and reports invalid data. 5) Performance testing : make sure that data is loaded in data warehouse within prescribed and expected time frames to confirm improved performance and scalability. 6) Production Validation Testing: Validate the data in production system compare it against the source data. 7) Data Integration Testing : make sure that the data from various sources has been loaded properly to the target system and all the threshold values are checked. 8) Application Migration Testing : In this testing, it is ensured that the etl application is working fine on moving to a new box or platform. 9) Data constraint Check : The datatype, length, index, constraints, etc.
Data input is taken from customer requirements and different data sources and new data warehouse is built and verified with the help of etl tools. In this type of project customer will have an existing dw and etl performing the job but they are looking to bag new tool in homework order to improve efficiency. Change request, in this type of project new data is added from different sources to an existing. Also, there might be a condition where customer needs to change their existing business rule or they might integrate the new rule. Report Testing, report is the end result of any data warehouse and the basic propose for which dw builds. The report must be tested by validating layout, data in the report and calculation. Etl process note : Click on the image for enlarged view). Etl testing Techniques 1) Data transformation Testing : Verify that data is transformed correctly according to various business requirements and rules.
Most of the companies are taking a step forward for constructing their data warehouse to store and monitor real-time data as well as historical data. Crafting an efficient data warehouse is not an easy job. Many organizations have distributed departments with different applications running on distributed technology. Etl tool is employed in order to make a flawless integration between different data sources from different departments. Etl tool will work as an integrator, extracting data from different sources; transforming it into the preferred format based on the business transformation rules and loading it in cohesive db known are data warehouse. Well planned, well defined and effective testing scope guarantees smooth conversion of the project to the production. A business gains the real buoyancy once the etl processes are verified and validated by an independent group of experts to make sure that data warehouse is concrete and robust. Etl or Data warehouse testing is categorized into four different engagements irrespective of technology or etl tools used: New Data warehouse testing, new dw is built and verified from scratch.
Tutorial 5 : Top 10 etl testing tools, it has been observed that Independent Verification and Validation is gaining huge market potential and many companies are now seeing this as prospective business gain. Customers have been offered a different range of products in terms of service offerings, distributed in many areas based on technology, process, and solutions. Etl or data warehouse is one of the offerings which are developing rapidly and successfully. Through etl process, data is fetched from the source systems, transformed as per business rules and finally loaded to the target system (data warehouse). A data warehouse is an enterprise-wide store which contains integrated data that aids in the business decision-making process. It is a part of business intelligence. What you will learn: Why do organizations need Data warehouse? Organizations with organized it practices are looking forward to creating the next level of technology transformation. They book are now trying to make themselves much more operational with easy-to-interoperate data.
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Etl testing / father's Data warehouse Process and Challenges: Today let me take a moment and explain my testing fraternity about one of the much in demand and upcoming skills for my tester friends. Etl testing (Extract, Transform, and load). This tutorial will present you with a complete idea about etl testing and what we do to test etl process. Complete list Tutorials in this series: Tutorial 1 : etl testing Data warehouse testing Introduction guide. Tutorial 2 : etl testing Using Informatica powerCenter tool. Tutorial 3 : etl. Db testing, tutorial 4 : Business Intelligence (BI) Testing: How to test Business Data.