Data transformation is an integral step in many processes employed by organizations today to harness the insights offered by big data, like data integration, warehousing, migration and wrangling. After transformation, data becomes more secure, accessible and usable for a variety of purposes. This process is performed to ensure the compatibility of data with different forms, while blending it with other information or migrating it.
Data transformation facilitates data conversion, irrespective of its form, to be stored, integrated, mined and analyzed for business intelligence.
Data Transformation Definition
The process of converting, cleansing and structuring data in formats that are useful for analysis is called data transformation. It is used when data needs to be altered to match the destination system. The transformed data can be used to augment the decision making processes in an organization, providing actionable insight and propelling growth.
Data transformation typically occurs at two places in the data pipeline. Organizations that store data on-site use an extract, transform, load procedure.
Most organizations today use data warehouses hosted on the cloud, which means they use a type of transformation called extract, load and transform, where the raw data is converted as it is uploaded.
Types of Data Transformation
There are typically four types of data transformation, classified based on their purpose:
Constructive: When data is replicated, copied or added
Destructive: When fields or records in the data are deleted
Structural: When data is reorganized by moving, renaming or combining columns
Aesthetic: When data is standardized to meet particular standards or parameters
Data Transformation Tools: Qlik, Talend and Microsoft
Data transformation tools are designed to manipulate, convert, and prepare data for analysis or storage in a desired format. Some common capabilities of data transformation tools include:
- Data Cleansing: Identifying and correcting errors or inconsistencies in the data such as missing values, duplicates, or outliers.
- Data Integration: Combining data from different sources into a unified format or structure.
- Data Enrichment: Enhancing the existing dataset with additional information from external sources to provide more context or insights.
- Data Aggregation: Summarizing or aggregating data to a higher level of granularity, for example, aggregating daily sales data into monthly or yearly totals.
- Data Normalization: Standardizing data formats, units, or representations to ensure consistency and comparability.
- Data Masking/Anonymization: Protecting sensitive data by replacing identifiable information with anonymized or masked values.
- Data Validation: Verifying the accuracy and integrity of data against predefined rules or criteria.
- Data Transformation: Converting data from one format or structure to another, such as converting raw transactional data into a star schema for use in a data warehouse.
Qlik Cloud Data Integration
The data transformation capability within Qlik Cloud Data Integration offers ELT functionalities tailored for cloud data warehouses and data lakes. Qlik empowers users with a pipeline that takes data from its raw form to a refined, analytically useful state.
This service boasts several key features:
Cloud-centric: Users can design, deploy, and oversee data pipelines directly within Qlik Cloud, leveraging SQL for transformation tasks on leading cloud platforms.
Adaptable, template-based methodology: Users can craft reusable transformations, establish rules, and generate custom templates, streamlining and expediting the creation of data projects.
Automation: Common practices and DataOps principles are automated, enabling swift and dependable operationalization of transformation tasks.
Integration: Tasks within the pipeline are seamlessly integrated with data movement services, ensuring the conversion of data into analytics-ready formats in the designated cloud environment, often in real-time.
The data transformation service offers the following features:
- Development of adaptable, tailored data pipelines
- Row-level transformations based on predefined rules
- Generation of new derived datasets utilizing:
– Source-to-target mappings
– Custom SQL queries for intricate logic
– Automatic creation of star schema data marts
– Establishment of logical relationships among datasets
- Option to materialize datasets as tables or generate them as views
- Support for change data capture (CDC) ensuring real-time and incremental data updates
- Execution of SQL operations directly on cloud data warehouse platforms (such as Snowflake, Azure Synapse, Google BigQuery, and Microsoft SQL Server) for optimized performance.
Third-Party Data Transformation
In Qlik Cloud Data Integration, third-party transformation refers to the process of integrating pre-existing data already loaded onto the selected cloud platform using external tools (such as Qlik Replicate and Talend).
This capability enables users to construct workflows atop existing data and integrate it into data pipelines without duplicating processes or double-consuming data. Tasks like creating transformation processes, data cleansing, and automating data warehousing are included.
Examples of scenarios for third-party transformation are:
- Transitional phases during migration from legacy tools to Qlik Cloud Data Integration.
- Utilizing an established cloud data warehouse or data lake for new project requirements.
- Enabling Qlik Cloud Data Integration to interface with proprietary solutions where direct connectivity isn’t feasible.
Third-party transformation aligns with Qlik’s core principles: leaving data in place, registering it, understanding its nuances, enhancing its quality, and commencing delivery processes.
QlikSense
Data Profiling: Qlik Sense’s data profiling capabilities go beyond basic statistics, providing detailed insights into data quality, distribution, and patterns. Users can identify outliers, missing values, and anomalies, enabling informed data cleansing decisions.
Associative Data Model: Qlik Sense’s associative engine allows for bidirectional exploration of data relationships, empowering users to uncover hidden insights and make connections between seemingly unrelated data points.
ETL (Extract, Transform, Load): Qlik Sense’s ETL capabilities encompass a wide range of transformations, from simple data cleansing to complex data enrichment using scripting and visual tools. Users can handle diverse data sources and structures with ease.
Data Governance: Qlik Sense offers robust data governance features, including role-based access control, data lineage tracking, and automated data quality checks. Organizations can ensure data integrity, security, and compliance throughout the data transformation process.
Talend Data Integration
Graphical Design Interface: Talend’s graphical interface offers an intuitive way to design data integration and transformation workflows, with drag-and-drop components and visual representations of data pipelines. Users can quickly build complex data transformations without writing code.
Rich Library of Connectors: Talend’s extensive library of connectors covers a wide range of data sources and systems, including databases, cloud platforms, applications, and APIs. Users can seamlessly integrate and process data from disparate sources with ease.
Data Quality: Talend’s data quality features enable users to define and enforce data quality rules, perform data cleansing and standardization, and validate data against predefined criteria. Users can ensure the accuracy, consistency, and completeness of their data.
Big Data Support: Talend provides specialized components and connectors for integrating and transforming big data technologies such as Hadoop, Spark, and NoSQL databases. Users can leverage the power of big data for advanced analytics and insights generation.
SQL Server Integration Services
Graphical Development Environment: SSIS’s visual development environment offers a rich set of tools for designing, debugging, and deploying ETL workflows. Users can create data flow tasks using drag-and-drop components and easily configure data transformations.
Built-in Transformations: SSIS includes a vast array of built-in transformations for data cleansing, aggregation, and transformation. Users can apply complex business logic, data validations, and error handling within their ETL processes.
Connectivity: SSIS supports connectivity to a wide range of data sources and destinations, including relational databases, flat files, XML files, and cloud services. Users can easily extract data from disparate sources and load it into their target systems.
Scalability: SSIS’s scalability features allow users to optimize performance and resource utilization for large-scale data transformation tasks. Users can parallelize data processing, implement partitioning strategies, and distribute workloads across multiple servers for efficient execution.
Azure Data Factory
Cloud-Based ETL: ADF provides a cloud-native platform for building, orchestrating, and managing data pipelines in Azure. Users can create complex ETL workflows using a visual interface and deploy them as scalable cloud services.
Integration with Azure Services: ADF seamlessly integrates with other Azure services, enabling users to leverage Azure’s rich ecosystem for data storage, processing, and analytics. Users can access Azure Blob Storage, Azure SQL Database, Azure Data Lake Storage, and other services directly within their data pipelines.
Data Transformation Activities: ADF offers a wide range of activities for data transformation, including data conversion, data manipulation, and data enrichment. Users can define custom data transformation logic using SQL, .NET, or Python scripts, ensuring flexibility and extensibility.
Monitoring and Management: ADF provides comprehensive monitoring and management capabilities, allowing users to track pipeline execution, monitor performance metrics, and troubleshoot issues in real-time. Users can set up alerts, view execution logs, and analyze pipeline activity to ensure reliable and efficient data transformation processes.
How Technoforte Can Help You
Unlock the true potential of your data with Technoforte’s cutting-edge data transformation services. As a premier business intelligence and data analytics company, we specialize in harnessing the power of data to drive actionable insights and transformative outcomes for businesses of all sizes.
At Technoforte, we offer a comprehensive suite of data transformation services designed to streamline your data processes and unlock the full value of your information assets. Our services include:
ETL (Extract, Transform, Load) Processes: We seamlessly extract data from diverse sources, transform it into actionable insights using advanced analytics techniques, and load it into your desired destination.
Data Cleansing and Enrichment: Our expert team ensures your data is clean, accurate, and enriched with relevant information, enabling you to make informed decisions based on trustworthy insights.
Data Integration and Aggregation: We integrate disparate datasets and aggregate them into a unified format, providing a holistic view of your business operations and enabling cross-functional analysis.
Real-time Data Processing: With our real-time data processing capabilities, you can access up-to-the-minute insights and respond swiftly to changing market dynamics.
Data Governance and Compliance: We implement robust data governance practices to ensure data security, compliance with regulatory requirements, and protection of sensitive information.
Why choose Technoforte for your data transformation needs? Our unparalleled expertise, innovative solutions, and commitment to customer satisfaction set us apart. With Technoforte, you can trust that your data is in safe hands, allowing you to focus on driving business growth and innovation. Experience the power of data transformation with Technoforte today.
Speak to our experts today to get a quote. Learn more here!
