In the world of big data where data is streaming in massive volume from multiple sources, Data Compatibility becomes a challenge for you. This is where Data Transformation comes into play. You need to transform your data to ensure Data Compatibility. Companies with On-premises Data Warehouses and Custom Integration solutions often use a Data Replication process to transform data before loading it to its destination.
In this blog, you will learn about transforming data in detail. You will also go through the needs, types, benefits, and challenges of Data Transformation. If you want to take a deep dive into the process of transforming data, then you are at the right place.
Data Transformation can be achieved by manually writing scripts or by using an ETL tool. And, with an ETL tool, you can ease the process So, if you are looking for an ETL tool that can automate the transformation of your data, then try Hevo.
Table of Contents
What is Data Transformation?
Data Transformation refers to the process of converting or transforming your data from one format into another format. It is one of the most crucial parts of data integration and data management processes, such as data wrangling, data warehousing, etc. Data transformation can be of two types – simple and complex, based on the necessary changes in the data between the source and destination.
Transformation of data allows companies to convert data from any source into a format that can be used in various processes, such as integration, analysis, storage, etc. You can use any ETL tool to automate your transformation or use any scripting language, like Python for manual data transformation.
Why Need to Transform Data?
There can be various reasons why you want to transform your data. Some of the most popular reasons are listed below:
- Companies need to transform their data to make it compatible with other available data so that they can aggregate the information and have a better analysis.
- At times, you want to move your data to a new source, such as a cloud data warehouse, which requires a change of the data type.
- You want to consolidate structured and unstructured data.
- You want to add more information to your data such as timestamps, to enrich it.
How to Transform Data?
Data Transformation can help analytic and business processes run more efficiently and enable improved data-driven decision-making. Data type conversion and flattening of hierarchical data should be included in the first phase of data transformations. These processes alter data in order to make it more compatible with analytics software. Additional transformations can be applied as needed by Data Analysts and data scientists as distinct layers of processing. Each processing layer should be built to do a specified set of operations in order to satisfy a recognized business or technological requirement.
Within the Data Analytics Stack, Data Transformation serves a variety of purposes.
Hevo is a No-code Data Pipeline. It supports pre-built data integrations from 100+ data sources. Hevo offers a fully managed solution for your data migration process. With Hevo, you can transform and enrich your data in minutes.
It will automate your data flow without writing any line of code. Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully-automated solution to manage data in real-time and always have analysis-ready data in your desired destination.
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Let’s look at some salient features of Hevo:
- Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
- Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within pipelines.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
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What are the steps involved in Data Transformation Process?
It is a crucial part of ETL (Extract, Transform and Load). Data extracted from the source is raw and needs changes before delivering it to the target. The process of transforming data is elaborated using the following steps:
- Data Discovery: It is the first step of your transformation process. It involves identifying and understanding data in its source format. You can use a manually written script or data profiling tools to get a better understanding of the structure of data, and then decide how it needs to transform.
- Data Mapping: It is the process of defining how each field is mapped, joined, filtered, modified, etc. to get the desired format.
- Code Generation: It is the process of generating or developing executable code, such as SQL, R, Python, etc. which can transform your data based on the mapping rules specified by you.
- Code Execution: In this process, you will execute the generated code on the data to create the desired output. The code integrates tightly into the transformation tool or uses separate steps to execute the code manually.
- Data Review: The final step of transformation focuses on ensuring that output data meets your transformation requirements. If any error or anomaly is found in the data, it is communicated to the developer or data analyst.
Data Transformation can be harder than it looks. For this reason, transforming data accurately requires tools that offer top-notch capabilities with zero data loss and no compatibility issues. To have a clear understanding, you can check out our exclusive list of the 8 Best Data Transformation tools in the market.
What are the Methods of Data Transformation?
- Aggregation: Data aggregation is the process of gathering raw data and presenting it in a summarized form for statistical analysis. Raw data, for example, can be aggregated over a period of time to produce statistics like average, minimum, maximum, sum, and count. You can analyze the aggregated data to gain insights about specific resources or resource groups after the data has been aggregated and written as a report. Data aggregation can be divided into two categories: time aggregation and spatial aggregation.
- Attribute Construction: This technique aids in the development of a data mining process that is both efficient and effective. New attributes are constructed and added from the given set of attributes to aid the mining process in attribute construction or feature construction of data transformation.
- Discretization: Data discretization is the process of breaking up continuous data attribute values into a finite number of intervals and assigning a particular data value to each interval. There are many different discretization methods available, ranging from simple equal-width and equal-frequency methods to much more complex methods like MDLP.
- Generalization: The method of generating successive layers of summary data in an evaluation database to obtain a more comprehensive view of a problem or situation is known as data generalization. Online Analytical Processing benefits from data generalization (OLAP). The main purpose of OLAP is to provide quick answers to multi-dimensional analytical queries. The technique can also be used to implement online transaction processing (OLTP). The term “OLTP” refers to a type of database system that is used to manage and facilitate transaction-oriented applications, particularly those that involve data entry and retrieval.
- Integration: Data integration is a critical step in data pre-processing that entails combining data from various sources and providing users with a unified view of the information. It combines data from multiple databases, data cubes, or flat files. For data integration, there are two main approaches: tight coupling and loose coupling.
- Manipulation: The act of changing or modifying data to make it more comprehensible and organized is known as data manipulation. Data analysis tools assist in finding patterns in the data and transforming it into a format that can be used to produce insights on financial data, customer behavior, etc.
- Normalization: Data normalization is a technique for converting source data into a more usable format for processing. The main goal of data normalization is to reduce or eliminate duplicate data. It has many benefits, including improving the efficiency of data mining algorithms and speeding up data extraction, among others.
- Smoothing: A method for finding trends in noisy data where the trend’s shape is unknown is data smoothing. The approach can be used to spot trends in the stock market, the economy, consumer sentiment, etc.
What are the Techniques of Data Transformation?
Rarely is raw data organized or formatted in a way that supports the aforementioned use cases. The following are a few typical instances of transformations that make data more immediately useful.
- Revising: Data is revised to ensure that values are correct and organized in a way that makes sense for their intended use. One method of revising data in database normalization involves stripping a data model of all redundancies and one-to-many values in each column. Normalization decreases storage requirements while also making a data model more concise and understandable to analysts. It is, however, a time-consuming process that necessitates extensive research, reverse engineering, and critical thinking.
- Data values are converted for formatting compatibility during data cleansing.
- Incompatible characters are replaced, units are converted, date formatting is changed, and other data types are altered during format revision or conversion.
- Key restructuring transforms values with inherent meanings into generic identifiers that can be used as fixed, distinctive keys across all tables.
- Deduplication is the process of locating and eliminating duplicate records.
- Data validation determines whether a record is valid based on how complete the data is, typically by excluding records with missing data.
- You can choose the fields you want to use as features, or the input variables to a predictive model, by removing redundant and inactive columns. It can also help a model’s performance and overall legibility.
- Computing: Calculating rates, proportions, summary statistics, and other significant figures is a frequent application of computing new data values from existing data. Another is to convert unstructured data, like that from media files, into structured data that a machine learning algorithm can understand.
- Simple cross-column calculations are included in the derivation.
- Summarization is the process of generating summary values using aggregate functions.
- By pivoting, column values become row values and vice versa.
- Records are organized in some order through sorting, ordering, and indexing to enhance search performance.
- In Z-score normalization, scaling, standardization, and normalization put numbers on a consistent scale, such as fractions of a standard deviation. This makes it possible to compare different numbers to one another.
- Vectorization is the process of converting non-numerical data into numerical arrays. These transformations have numerous machine-learning uses, including image recognition and natural language processing (NLP).
- Separating: By breaking down values into their component parts, we can separate them. Because of idiosyncrasies in data collection, data values are frequently combined within the same field, but they may need to be separated for more granular analysis.
- For fields with delimited values or to convert a column with multiple possible categorical values into dummy variables for regression analysis, splitting a single column into multiple columns is a common practice.
- Data is excluded through filtering based on specific row values or columns.
- Combining: Bringing together data from various tables and sources to create a complete picture of an organization’s operations is a common and crucial task in analytics.
- Data from different tables are connected through joining.
- Records from various tables are combined during merging, also referred to as appending or union. You can assemble parts of the sales and marketing funnel by combining the two tables using a common column, such as “email” in the example below. This is also an example of integration, which entails matching names and values for the same data element in multiple tables.
What are the Types of Data Transformation?
There are 2 types of Data Transformation –
- Traditional or Batch
- Interactive
1. Traditional or Batch Data Transformation
Data transformation uses batch processes traditionally. It involves executing code and implementing transformation rules on your data in a data integration tool. Micro batch refers to the process of transforming and delivering data with low latency. The process is quick and easy. Companies have been using traditional data transformation for decades. But, it has a lot of limitations such as the involvement of personnel, cost, speed, etc. which reduces its efficiency.
2. Interactive Data Transformation
Interactive transformation allows companies to interact with datasets through a visual interface, such as to understand data, correct and change data through clicks, etc. In Interactive data transformation, all the steps are not followed linearly, and it doesn’t require specific technical skills.
It shows the user patterns and anomalies in the dataset to reduce errors in the data. There is no need for a developer in interactive data transformation, which reduces the time required to prepare and transform data. It gives business analysts the power to control and manage their datasets.
Tools for Transforming Data
These tools can convert or transform your data from one format into another format within minutes. As Data Transformation is considered to be one of the most crucial parts of Data Integration and Data Management processes, the tools play an important role. These tools should be capable of performing Data Enrichment, Data Cleansing, and Data Modification with zero data loss.
Let’s discuss some of the Top Tools for Transforming Data.
Hevo Data
Hevo is a No-code Data Pipeline. It supports pre-built data integrations from 150+ data sources. Hevo offers a fully managed solution for your data migration process. It will automate your data flow in minutes without writing any line of code. Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully-automated solution to manage data in real-time and always have analysis-ready data in your desired destination.
Informatica – PowerCenter
Informatica is an enterprise Data Integration tool that specializes in extracting, transforming, and loading data into your Data Warehouse. It offers optimized Cloud Data Management tools for huge data processing.
Informatica supports integration with various traditional databases including SQL and non-SQL databases in real-time and batch. Informatica provides enterprise-class data transformation for any type of data, format, size, and complexity.
You can map it once, and then deploy it anywhere without writing any line of code. But being an expensive tool with a challenging learning curve makes it less suitable for smaller organizations or companies with limited resources.
Talend
Talend is a Cloud-based open-source Big Data and Data Integration tool compatible with local and virtual Data Sources on the Cloud along with hundreds of pre-built integrations such as Google Cloud Platform, Amazon Web Services (AWS), Snowflake, etc.
Talend provides a variety of solutions for Data Extraction, Transformation, and Loading in a single platform. With Talend, you can enrich your job to include on-the-fly data transformation.
To meet the needs of larger enterprises, Talend has also introduced a paid Data Management program with additional tools and features for Design, Productivity, Management, Monitoring, and Data Governance.
What are the Benefits of Transforming Data?
Companies require data transformation to get massive benefits from the collected data. Let’s discuss some benefits of it.
- Enhanced Data Quality: As companies consolidate data from multiple sources, inconsistency, missing data, and null values become an issue. Transformation of data reduces these problems to enhance data quality for your analysis. It also allows data to be in a well-organized manner that makes it easy for us to use.
- Data Management: As data integrates from multiple sources, inconsistencies in metadata become a challenge to understand data. Data transformation helps you to refine metadata so that it becomes easier for you to understand your data set.
- Faster Queries: Your transformed data is stored at a location from where queries can be performed easily.
- Compatibility: With the transformation of data, you can achieve compatibility between applications, types of data, systems, etc.
How does Data Transformation Relate to Data Integration?
With every organization generating data like never before, it is essential to aggregate all the data in one place to extract valuable insights. This is called Data Integration, and Data Transformation is a very crucial step to unleashing its full potential.
Any Digital transformation is likely to fall short unless it is based on a solid foundation of Data Transformation. It is a crucial part of ETL (Extract, Transform, and Load), and ETL is a crucial part of Data Integration. Data extracted from the source is raw and needs changes before delivering it to the destination.
You need to transform your data to ensure Data Compatibility. This way, your data is high-quality for advanced analytics and AI. Any problem during the transformation can lead to data losses and mess up your Data Integration. Simply put, Data Transformation is the “T” in ETL or ELT.
To take advantage of data opportunities and overcome Data Integrity challenges, companies often adopt a Data Transformation Platform to transform data before loading it to its destination. But if you’re manually transforming your data, you need to take care of the Data Integrity and Incompatibility issues all by yourself.
What are the Challenges of Transforming Data?
While you are transforming data, you will place numerous challenges. Some of them are listed below:
- Time-Consuming: You need to extensively clean your data to transform, integrate or migrate it. This process can be tiring and time-consuming.
- Costly: Transforming data is an expensive process. It involves the cost of infrastructure, software, and tools. You need to hire a team of experts. Also, a lack of expertise can create huge and expensive problems.
- Slow Process: Data transformation in batches can cause a delay in decision-making as you have to wait for several hours before you start with the next batch.
- Undesired Format: Companies invest a few hours to transform data into one format for a specific application, and then revert it to its prior structure for a different application.
Conclusion
In this blog, you have learned about Data Transformation in detail. Transformation of data allows you to convert your data from various sources into the desired format. With data transformation, you can refine your data for data integration and data management. So, if you are looking for an ETL tool that facilitates the automatic transformation of data, then Hevo is the right choice for you.
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Hevo Data with its strong integration with 100+ Sources & BI tools allows you to not only export data from sources & load data in the destinations, but also transform & enrich your data, & make it analysis-ready so that you can focus only on your key business needs and perform insightful analysis using BI tools.
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Tell us about your experience with Data Transformation in the comment section below.
Driven by a problem-solving ethos and guided by analytical thinking, Oshi is a freelance writer who delves into the intricacies of data integration and analysis. He offers meticulously researched content essential for solving problems of businesses in the data industry.