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Data transformation can be achieved by using Python or SQL scripts to extract data and transform it.
The ETL (Extract, Transform, Load) tools hosted on the cloud are the tools which leverage the expertise and infrastructure of the vendor. Denizon offers a cloud-based ETL tool that comes to your rescue for all your data transformation needs.
Deploying on-premise ETL tools can also take away the pain of data transformation by automating the process. However, these tools are deployed in your company’s site and require extensive infrastructure costs.
It is established that the world has a lot of data, and a minimal amount of it is put into use. But the question here is, why do you need to transform data? There might be many reasons why:
When you are moving your application as a whole into the cloud, or a new data store in the cloud, you will need to change the type of data that you have.
If you want to aggregate and compare data from different sources, then data transformation is the way to do that.
If you want to add information to your data or enrich it, then data transformation is useful in such a scenario. Data transformation helps when you want to perform lookups, add timestamps or add geolocation data to your existing data.
If you have some form of streaming or unstructured data that you want to analyse along with some structured data that you have, then you can perform data transformation to achieve that.
Transforming your data requires a team of experts in the domain and substantial infrastructure costs.
Before transforming the data, you need to cleanse it extensively. This is a time-consuming process, especially when you are dealing with unstructured data.
The process of transforming data and extracting it is slow. The transformation is done in batches and can require as much as 24 hours for one batch to complete.
Key features include:
Our team of experts helps you expedite your entire data transformation process by understanding the needs of your company.
We plan, interpret your data, translate your data and ensure that the final output lives up to your expectations in terms of quality.
Your data transformation process becomes cheaper with us. You can leverage the expertise of our team for your transformation process, and you will not require to set up your very own team. Additionally, since Denizon offers a cloud-based solution, you can bid farewell to expensive infrastructure and the cost of maintaining an on-premise ETL tool.
We have the expertise and experience, so we are fast. We can extract your data, transform it and load it in near real time.
Make your critical business decisions without having to worry about the time consumption for the data transformation process.
Denizon is 100% secure and is compliant with all the regulatory compliances.
With us, stay assured that your data will remain secure.
The first step in the process is to interpret your existing data correctly. We help you determine the kind of data you currently have, and the final output format that you want to achieve.
Data interpretation can be a difficult thing to achieve as the mere extension of a file cannot determine the kind of data inside. Applications generally determine the kind of data based on the extension applied to it. But the problem here is that users can add the desired extension to a file, and it does not always mean that the file has that kind of content.
Hence, accurate data interpretation requires tools that can peer deep inside the structure of a file and its database to determine what is really inside.
Another step in the data interpretation process is to determine the target format. The target format is how your data will be after the transformation process is complete. We help you determine the target format by understanding the system that will receive your transformed data.
Once we help you determine the final transformation format, we help you run quality checks on your data. A quality check helps you determine problems like missing text and corrupt values in the database. This is the point where your data needs a bath. Carrying out data quality checks, translating, and cleansing data early in the transformation process helps you ensure that the bad or corrupted data does not end up posing problems in your transformation process later.
After maximising the quality of your source data, you can begin the actual data transformation process. This process involves taking each part of your source data and replacing it with data that fits the requirements of the final format that you require.
Data translation however is not just limited to replacing individual data pieces with another piece but also involves restructuring the overall file for it to be usable finally. For example, a CSV file separated by commas might need to be converted to an XML file to organise information with the help of tags.
After the translation is complete, we help you ensure the translated data is maximally useful or not. This post-translation data quality check process will show the inconsistencies, the missing information and other errors that might have been introduced in the data transformation process.
Even if your data was error-free during the translation process, there is still a chance that problems have been introduced along the way. Hence, the post-translation quality check process is extremely important to ensure that the final data is actually usable.
We:
We are faced with an ocean of data to process, but we don’t just jump into the bolts and nuts of data transformation. We engage business users, understand business processes, develop insights, and design the target format of the data before we start working on it. We start with “dimensional modelling” which
Another best practice that we adopt for data transformation is data profiling. Data profiling helps us understand the state of the raw data and the amount of work that needs to be put in before making the data is ready for analysis. Basically, we get know the ins and outs of your data before we start transforming it.
Another good practice we follow for data transformation is a data bath. After obtaining the insights from data profiling, it is easier to understand the amount of data transformation work to be carried out on the data. If your data has a large frequency of missing values or junk data, then you might need to give your data a bath and include these missing values. We help you clean data early in the transformation process, thus making it easier to ensure that bad data does not end up in the final output.
Dimensions help put context to the data. For example, dates, products, and customers are dimensions, while the sales results are facts. Putting dimensions around facts makes data meaningful. Sales data would not be beneficial if it was not linked to any dimension. Hence, building dimensions before facts are what makes the data transformation process easier and mapable.
We record audit and data quality metrics in the process of data transformation to capture the number of records loaded at each step of the transformation process and the time at which the steps happened.
Capturing data quality test results and including them in the audit records provides the ability to reconstruct the lineage of the data. It enables analysts to work backward and have reliable answers about the history of where the data comes from.
Another best practice that we adopt for the data transformation process is to engage the user community continuously. The measure of data transformation is the extent to which the target user community accepts and uses the transformed data. The transformed data undergoes extensive acceptance testing, and we fix the defects found by business users. We engage customers and users and maximise the usability of the final data.
Data transformation is a huge, bulky, time-consuming process that requires expertise. Outsourcing this process helps you and your organisation focus on critical business decisions instead. With the best practices mentioned above, combined with our expertise, we help you transform your data with ease.
Get advice on Interpreting Data, Checking Data, Translating Data, Data Profiling, and Data Cleansing.
