It extends Azure Synapse with best practices and DataOps, for agile data development with built-in data governance functionalities. BryteFlow extracts and replicates data in minutes using log-based Change Data Capture and merges deltas automatically to update data. It can be configured with times series as well. There's no coding for any process just point and select!
BryteFlow supports enterprise-scale automated data integration with extremely high throughput, ingesting terabytes of data, with smart partitioning, and multi-threaded, parallel loading. Because Hyper-Q returns results that are bit-identical to Teradata, existing applications can be replatformed to Azure Synapse without any significant modifications. With Datometry, enterprises can move to Azure rapidly and take full advantage of Synapse immediately. Denodo Denodo provide real-time access to data across an organization's diverse data sources.
It uses data virtualization to bridge data across many sources without replication. Denodo offers broad access to structured and unstructured data residing in enterprise, big data, and cloud sources, in both batch and real time. Dimodelo Dimodelo Data Warehouse Studio is a data warehouse automation tool for the Azure data platform.
Dimodelo enhances developer productivity through a dedicated data warehouse modeling and ETL design tool, pattern-based best practice code generation, one-click deployment, and ETL orchestration. Dimodelo enhances maintainability with change propagation, allows developers to stay focused on business outcomes, and automates portability across data platforms. Fivetran Fivetran helps you centralize data from disparate sources. It features a zero maintenance, zero configuration data pipeline product with a growing list of built-in connectors to all the popular data sources.
Setup takes five minutes after authenticating to data sources and target data warehouse. HVR HVR provides a real-time cloud data replication solution that supports enterprise modernization efforts. The HVR platform is a reliable, secure, and scalable way to quickly and efficiently integrate large data volumes in complex environments, enabling real-time data updates, access, and analysis.
Global market leaders in various industries trust HVR to address their real-time data integration challenges and revolutionize their businesses. Incorta Incorta enables organizations to go from raw data to quickly discovering actionable insights in Azure by automating the various data preparation steps typically required to analyze complex data. Using a proprietary technology called Direct Data Mapping and Incorta's Blueprints pre-built content library and best practices captured from real customer implementations , customers experience unprecedented speed and simplicity in accessing, organizing, and presenting data and insights for critical business decision-making.
Informatica Cloud Services for Azure Informatica Cloud offers a best-in-class solution for self-service data migration, integration, and management capabilities. Customers can quickly and reliably import, and export petabytes of data to Azure from different kinds of sources. Stitch has pricing that scales to fit a wide range of budgets and company sizes. All new users get an unlimited day trial.
Enterprise plans for larger organizations and mission-critical use cases can include custom features, data volumes, and service levels, and are priced individually. Which tool is best overall? That's something every organization has to decide based on its unique requirements, but we can help you get started.
Sign up now for a free trial of Stitch. Select your integrations, choose your warehouse, and enjoy Stitch free for 14 days. Set up in minutes Unlimited data volume during trial. About Features table Transformations Data sources and destinations Support, documentation, and training Pricing.
About Ab Initio Ab Initio is an on-premises data integration tool. Let's dive into some of the details of each platform. Transformations Ab Initio Ab Initio can perform a wide range of preload transformations through a graphical interface in its Business Rules Environment.
Informatica Informatica has been an on-premises product for most of its history, and much of the product is focused on preload transformations, which is an important feature when sending data to an on-premises data warehouse. Stitch Stitch is an ELT product. Try Stitch for free for 14 days Unlimited data volume during trial Set up in minutes.
Connectors: Data sources and destinations Each of these tools supports a variety of data sources and destinations. Ab Initio Ab Initio does not publicly disclose how many data sources it supports, but the sources include databases, message queuing infrastructure, and SaaS platforms.
Informatica Informatica provides Cloud Connectors for more than applications and databases. Stitch Stitch supports more than database and SaaS integrations as data sources, and eight data warehouse and data lake destinations.
However, I will admit that even in these cases, the workload is always balanced between the product and the approach — but the approach must always be adaptive, regardless of the technology — or you have simply misunderstood the nature of the beast you are taming. What is an adaptive approach? Lessening the rigidity of a design with the assumption that something will change.
In short, building the right to change and retry into the design itself. This approach is so foreign to most technologists that even its mention can draw volcanic wrath. How does it work? A number of years ago, American auto makers discovered that Japanese auto makers were using adaptive approaches in their auto designs. They would build a little extra material into a fender, or pad a little here or there — so as the design progressed — minor corrections could be made with little to no penalty.
An adaptive model lessens the rigidity of the design so the team can maintain maximum forward momentum with little to no penalty. The result: an environment that exists with the expectation of change, not the fear of it. Clearly we could implement Ab Initio wrong — and quite unfortunately some people do.
An adaptive development approach could save them from their lack of experience in data warehousing, but not the other way around. Rapid experimentation can resolve a lot of woe. Still others are out to make a mark — do something exciting with the product that nobody else has done. This is like doing something with Saran Wrap that nobody else has ever done.
Not that Ab Initio is as common as Saran Wrap, but data processing certainly is. However, a data warehousing effort is more than just developing a graph and solving a technical problem. Procedural issues face us also — such as operation and administration, change management and the like.
Ab Initio supports these issues but does not require you to obey them. Many people place little to no value on environmental issues. Verifying the data accuracy in data columns. For instance: the number of months column should not exceed a value greater than 12 months.
Verifying the missing data values in the column. This is used to check if there are any null values present or not. Ab initio ETL testing can be categorization on the basis of objectives testing and data reporting. The ETL testing is categorized on the below points. This type of testing category involves matching the count of data records in both the source and target systems. This type of testing category involves data validation between the source and the target systems.
Here it helps to perform data integration and threshold data value check and also eliminate the duplicate data value in the target system.
This type of testing category confirms the data mapping of any object in both the source and target systems. This also involves checking the functionality of data in the target system.
This involves the generation of data reports for end-users to verify if the available data in the reports as per your requirements. With the help of this testing, you can also find the deviations in the report and cross-checks the data in the target system to validate the report. It involves fixing the bugs and defects in the data in the target system and running the data reports to perform data validation.
This involves testing all the individual source systems and then combines the results to find if there are any deviations. There are three main approaches available here:. ETL testing can also be divided into the following categories:.
In this type of ETL testing, your new data warehouse system can be built and verify the data. Here the input data can be taken from end-users or customers forms the different data sources then a new data warehouse is created. In this type of testing, customers will have an existing data warehouse and ETL tool, but here they look for a new ETL tool to improve the data efficiency.
This testing involves data migration from the existing system by using a new ETL tool. In this change testing, new data will be added from the different data sources to an existing system. Here customers can also change the existing ETL rules or new rules can also be added. In this type of testing, the user can create reports to perform data validations. Here the reports are the final output of any data warehouse system. Report testing can be done on the basis of layouts, data reports, and calculated values.
Ab Initio ETL testing techniques play a vital role and you need to apply these techniques before performing the actual testing process. These testing techniques should be applied by the testing team and they must be aware of these techniques. There are various types of testing techniques available, they are:.
This type of testing technique is used to perform analytical data reporting and analysis function, and also checks for the validation data production in the system.
The production validation testing can be done on the data that is moved to the production system. This technique is considered to be a crucial testing step as it involves a data validation method and compares these data with the source system. This type of testing can be performed when the test team has less to perform any type of testing operations. The targeted testing checks the data count in the source as well as target systems.
In this type of testing, a test team involves invalidating the data values from the source system to the target system. This checks the corresponding data values also available in the target system. This type of testing technique is also time-consuming and mainly used to work on banking projects. In this type of testing, a test team will validate the data ranges.
All the threshold data values in the target system will be checked if they are expecting the valid output. With the help of this technique, you can also perform data integration in the target system where data is available from the multiple data source system once you finish the data transmission and loading operations. Application migration testing is normally performed when you move from an old application to a new application system. This type of testing saves a lot of your time and also helps in data extraction from legacy systems to a new application system.
This testing includes the various types of checks such as data type check, index check, and data length check. This testing technique involves checking for the duplicate data situated in the target system. When there is a huge amount of data residing in the target system. It is also possible that there are also duplicate data available in the production system. The following SQL statement used to perform this type of testing technique:. Duplicate data appears in the target system because of these reasons:.
If no primary key is specified, then the duplicate values may come. This also arises due to incorrect mapping and environmental data issues. Manual errors arise while transferring data from the source system to the target system. The data transformation testing is not performed by using any single SQL statement.
This testing technique is time-consuming and also helps to run multiple SQL queries to check for the transformation rules.
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