With the rapid development of computing capabilities and storage techniques, there is no doubt that we are facing a series of opportunities and challenges brought about by the data era. The new data-based trend not only spreads wisdom into decision-making and performance improvement but also poses a great threat to traditional data processing techniques.
Data transformation is the process of changing the format, structure, or values of data in a new format. Furthermore, the rising complexity of reality requires simple and flexible solutions to drive businesses’ competition.
Benefits of Data Transformation
Whether it’s information about customer behaviors, internal processes, supply chains, businesses and organizations across all industries understand that data has the potential to increase efficiency and generate revenue.
By using a data transformation process, companies are able to reap massive benefits from their data, including:
- Managing Big Data more effectively: With data being collected from different sources, inconsistencies in metadata can make it a challenge to understand data.
- Data transformation organises better metadata to make it easier to organize and understand what’s in the data set and what drives the client’s business.
- Performing faster queries: Transformed data are standardized and stored in virtual machines, where they can be quickly and easily retrieved.
- Enhancing data quality: Data quality is becoming a major concern for organizations due to the risks and costs of using bad data to obtain business intelligence.
Data transformation can be used in different industries: from healthcare to financial services. Then, there are some key aspects we should consider before working on data:
- Determine business requirements
- Understand and profile your data sources
- Determine data extraction methods
- Establish data transformation requirements
- Decide how to manage the ETL process/li>
Extract-Transform-Load (ETL) processes are used to extract, clean, transform, and load Big Data from source systems for cohesive integration, bringing it all together to build a unified source of information for business intelligence (BI). As a vital stage of the ETL process, data transformation is necessary to change the information into a format that a business intelligence platform can interact with actionable insights.
The Extraction
Before organizing the data, the first step in the ETL process is extracting the raw data from all the relevant sources for the analysis. The data sources may include:
- CRM systems
- marketing automation platforms
- cloud data warehouses
- unstructured and structured files
- on-premise databases
- cloud applications, and any other data sources able to drive useful insights.
Once all the data has been consolidated, we notice that data from different sources are dated and structured in different formats.
In this step, the data must be organized according to size, and source to suit the transformation process. There is a certain level of consistency required in all the data to be extracted into the system and processed in the next step.
The complexity of this step can vary significantly, depending on data types, the volume of data, and data sources. Although we should consider several factors, scalability is crucial.
Be highly scalable means to be able to extract and process massive amounts of data in a short time.
Data Transformation
Data Transformation is the second step of the ETL process in data integrations.
Data needs to be cleansed, mapped and transformed. In fact, this is the key step where the ETL process adds value and changes data such that insightful BI reports can be generated. It may involve following processes/tasks:
- Filtering – loading only certain attributes into the data warehouse.
- Cleaning irrelevant data from the datasets – filling up the NULL values with some default values
- Joining – joining multiple attributes into one.
- Splitting – splitting a single attribute into multiple attributes.
- Sorting – sorting tuples on the basis of some attribute (generally key-attribute).
Quality data sources won’t require many transformations, while other datasets might require it significantly. To meet the target database’s technical and business requirements, we can adopt several transformation techniques.
The level of manipulation required in ETL transformation depends on the data extracted and the needs of the business.
Loading VS Streaming
The concluding step in the three-step ETL process is the act of loading/streaming the datasets that have been extracted and transformed earlier into the target database.
There are two ways to go about it; the first is a SQL insert routine that involves the manual insertion of each record in every row of the target database table. The other loading approach uses a process called a bulk load of data, reserved for massive data loading.
The SQL is slow, but it conducts data quality checks with each entry. While the bulk load is much faster for loading massive amounts of data, it does not consider data integrity for every record. Bulk loading is ideal for datasets you’re confident are free of errors.
You can use the following mechanisms for loading a data warehouse:
Loading a Data Warehouse with SQL Loader
- Loading a Data Warehouse with External Tables
- Loading a Data Warehouse with Direct-Path APIs
- Loading a Data Warehouse with Export/Import
ETL Streaming
Streaming ETL process is useful for real-time use cases: dashboard, dynamic insights in particular for the customer experience. Fortunately, there are tools that make it easy to convert periodic batch jobs into a real-time data pipeline.
Transformation and load data can be extracted using a stream-based data pipeline to perform SQL queries and generate reports and dashboards.
The streaming application ETL can extract data from any source and publish it directly to the streaming ETL application, or the source can publish the data directly to the streaming ETL application and extract it from another source. Apache Kafka is a popular tool for real-time data processing, but also Amazon MQ, IBMQ. We can extract data with and allow ETL to stream in the cloud in real-time, without the need for complex systems that require coding.
The ETL architecture for streaming is scalable and manageable, offering a wide variety of ETL scenarios, including a variety of data types.
The new competitive scenario will depend on how organisations use large volumes of Big Data to analyse, organize and restructure their business process.
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