Data collecting and processing have undergone a rebirth in the previous few decades, with data teams now having more information at their disposal than ever before. While this has increased data analytics and research, it has also brought several challenges for engineers and business teams. Raw data can be tough to deal with and filter. The issue isn’t always how to collect more data, but rather which data to store and evaluate. Businesses must use data transformation to curate relevant, useful data and make it usable across many systems. In this post, we will define Log and Data Transformation, as well as the different types, benefits, methods, and tools required to transform data.
What is Data Transformation?
The process of changing data from one format to another, often from the format of a source system to the required format of a destination system, is known as data transformation and can also be referred to as data manipulation. Most data integration and data management operations, such as data wrangling and data warehousing, need data transformation.
Data transformation is a step in the ELT/ETL process that can be classified as “simple” or “complex,” based on the kind of modifications that must be made to the data before it is sent to its destination. The data manipulation process can be automated, performed manually, or a combination of the two.
How Data Transformation Works
The data manipulation process’s purpose is to extract data from a source, convert it to a usable format, and transport it to a destination. This entire procedure is referred to as ETL (Extract, Load, Transform). During the extraction phase, data is recognized and extracted from several places or sources and stored in a single repository.
Data collected from the source site is frequently raw and unusable in its raw state. The data must be altered to circumvent this barrier. This is the most important phase in the ETL process since it allows your data to be mined for business insight. Several processes are taken during transformation to turn it into the required format. Data must be cleansed in some circumstances before it can be altered. Also, Data cleansing is the process of preparing data for transformation by removing inconsistencies or missing values.
Data Transformation Types
The following are the most typical types of data transformation:
#1. Batch data transformation
Batch data transformation, also known as bulk data transformation, is the process of changing data into groups over time. Traditional batch data transformation, which includes manual execution with programmed languages such as SQL and Python, is currently considered rather out of date.
#2. Interactive data transformation
As more businesses resort to cloud-based systems—IBM says that 81% of businesses utilize several cloud-based systems—data end-users are looking for more diverse techniques to transform data. The concepts of interactive data transformation, also known as real-time data transformation, are comparable to those of real-time integration and ELT processing.
Batch transformation is a subset of interactive data transformation. However, the steps are not always sequential. Interactive data transformation, which is gaining popularity due to its user-friendly visual interface, leverages previously written and examined code to find outliers, patterns, and problems in data.
Data Transformation Process
The data manipulation process in a cloud data warehouse is most commonly ELT (Extract Load Transform) or ETL (Extract Transform Load). With cloud storage costs falling by the year, many teams are opting for ELT, with the difference being that all data is loaded into cloud storage before being transformed and added to a warehouse.
The transformation process is typically divided into six stages:
- Data Discovery: The first stage involves data teams working to understand and locate relevant raw data. Analysts/engineers can have a better understanding of the alterations that must take place by profiling data.
- Data Mapping: Analysts determine how individual fields are updated, matched, filtered, merged, and aggregated during this phase.
- Data Extraction: Data is transported from a source system to a destination system during this step. Sources for extraction can be organized (databases) or unstructured (event streaming, log files).
- Code Generation and Execution: Once the raw data has been extracted and imported, it must be transformed to be stored in a manner suitable for BI and analytic applications. This is usually achieved by analytics engineers who programmatically change data using SQL/Python. This code is run on a daily/hourly basis to deliver timely and relevant analytic data.
- Review: Once the code has been implemented, it must be examined and checked to verify proper and appropriate implementation.
- Sending: The final stage is to transfer data to its intended destination. The goal could be a data warehouse or another structured database.
These stages are intended to demonstrate data manipulation patterns; there is no single “correct” transformation procedure. The best process is one that works well for your data team.
Log Data Transformation
Log data transformation is a type of data transformation in which a logarithmic function is applied to a dataset or individual data values. Logarithmic functions are mathematical functions that can be used to convert highly skewed data or data with a broad range of values into a more manageable and interpretable form.
The logarithmic function is applied to the data values in log data transformation, and the modified values are utilized in place of the original values. The natural logarithm (ln) and the base-10 logarithm (log10) are the most often used logarithmic functions.
Log data transformation is very beneficial when working with data with a wide range of values, where some values deviate greatly from others. The range of values is compressed and the discrepancies between values become more understandable by taking the logarithm of the data values. This can help with data visualization, pattern recognition, and statistical analysis.
Log data transformation is widely utilized in industries such as finance, economics, biology, and engineering. In finance, for example, stock prices are frequently highly skewed, with a few high-value equities having a substantial impact on the total dataset. Applying a log transformation to pricing can help with price analysis and comparison. Log transformation is used in biology to examine gene expression data because expression levels might vary greatly between genes.
Why Do Companies Require Data Transformation?
Every day, businesses generate massive amounts of data. information is, however, useless unless information can be used to gather insights and promote corporate progress. Data manipulation is used by organizations to change data into formats that may then be used for a variety of processes. There are several reasons why businesses should alter their data.
- Transformation makes diverse sources of data compatible with one another, making it easier to aggregate data for comprehensive analysis.
- Data migration is simplified since the source format can be translated into the target format.
- Data transformation aids in the consolidation of structured and unstructured data.
- The transformation procedure also allows for enrichment, which improves data quality.
The ultimate goal is to give enterprises with consistent, accessible data that provides reliable analytic insights and predictions.
Data Transformation Benefits
Businesses and organizations across all industries recognize that data can boost efficiencies and produce money, whether it is information about customer habits, internal operations, supply chains, or even the weather. The problem here is to ensure that all of the data obtained can be used. Companies can get significant benefits from their data by adopting a data transformation process, such as:
- Getting maximum value from data: According to Forrester, between 60% and 73% of all data is never examined for business intelligence. Companies might use data transformation tools to standardize data to increase accessibility and usability.
- More effective data management: As data is generated from an expanding number of sources, errors in metadata can make it difficult to organize and comprehend data. Data manipulation refines metadata to make it easier to organize and comprehend the contents of your data source.
- Performing speedier queries: Transformed data is standardized and kept in a source place where it can be retrieved quickly and easily.
- Improving data quality: Because of the risks and costs associated with using faulty data to generate business insight, data quality is becoming a major concern for enterprises. Data manipulation can decrease or remove quality issues such as discrepancies and missing values.
Drawbacks of Data Transformation
While there are various benefits to data transformation methods, it is crucial to note that there are a few potential drawbacks.
- Transformation can be expensive and resource-intensive: While processing and compute costs have decreased in recent years, it’s not uncommon to hear stories of outrageous AWS, GCP, or Databricks bills.
- Contextual awareness is critical: Extreme errors are conceivable if analysts/engineers changing data lack business context or understanding. While data observability tooling is improving, some mistakes are almost undetectable and could lead to inaccurate data interpretation or business decisions.
Techniques for Data Transformation
Data transformation techniques are used to clean and arrange data before storing it in a data warehouse or analyzing it for business intelligence. Not all of these strategies apply to all types of data, and in certain cases, more than one technique may be used. The following are some of the most prevalent techniques:
#1. Data Smoothing
Smoothing is a technique that uses an algorithm to eliminate noise from a dataset to find a trend. Noise carton your data, and removing or minimizing it allows you to extract superior insights or find patterns that you would not have seen otherwise.
#2. Attribution Construction
One of the most prevalent strategies in data transformation pipelines is attribution construction. The process of developing new features from a set of existing features/attributes in a dataset is known as attribute construction or feature construction.
#3. Data Generalization
The process of changing low-level attributes into high-level ones utilizing the concept of hierarchy is referred to as data generalization. Data generalization is used with categorical data that has a small number of different values.
#4. Data Aggregation
Data aggregation is one of the most widely used strategies in data transformation. When you apply data aggregation to raw data, you are storing and displaying data in a summary format.
#5. Data Discretization
The process of converting continuous data into a sequence of data intervals is referred to as data discretization. This is a very beneficial strategy for making data easier to study and analyze, as well as improving the efficiency of any applicable algorithm.
#6. Data Normalization
Last but not least, data normalization is the process of reducing the size of data without losing information to reduce or eliminate redundant data and increase algorithm efficiency and data extraction effectiveness.
#7. Data Integration
Data integration is a vital step in the pre-processing phase, not a data transformation technique. The process of merging data from numerous sources to generate a uniform view of the data is known as data integration.
#8. Data Manipulation
The process of making your data more legible and organized is referred to as data manipulation. This can be accomplished by modifying or changing your source datasets.
Tools for Data Transformation
In general, data transformation tools are pieces of software that automate the data transformation process so that it can be completed in minutes rather than hours. In reality, one of the most important processes in the data integration process is data transformation.
Today, there are numerous data transformation tools created for data modification, but not all of them are appropriate. You must hunt for a certain piece of software that fits your business plan and can assist you in reaching your end objective.
We’ve compiled a list of the finest data transformation tools in 2023 that are adaptable, efficient, and cost-effective for your company.
#1. Rivery
Rivery is a fully-managed DataOps platform and one of the best data transformation tools. It can effortlessly automate, maintain, and alter data models for any organizational data.
#2. DBT
When it comes to data transformation, Data Build Tool (DBT) is one of the easiest command tools on the market. This tool is particularly handy if you want to generate tables and views using incremental tactics.
#3. Qlik
Since 1993, Qlik has been involved in the field of business analysis. It is now one of the biggest software companies, providing various data solutions to bridge the gap between data, insights, and action.
#4. Matillion
Matillion was founded in early 2011 in Manchester, United Kingdom to provide business analytics as a service. Since then, they’ve grown to 500+ workers and a $1.5 billion valuation.
#5. Trifacta
Trifacta, an easy-to-use, visual data engineering and data wrangling cloud platform, is another of the top data transformation tools on our list. Also, Trifacta is ideal for data teams preparing, cleaning, transforming, and visualizing raw data.
#6. Informatica
Informatica is a cloud-based intelligent data management platform that transforms data in the cloud or on hybrid infrastructures. On this data transformation tool platform, pre-built transformations can be used to map data formats. There is no need for a code.
#7. Datameer
Datameer is a SaaS data transformation platform created for Snowflake, a significant data cloud provider. It covers your whole data life cycle journey within the Snowflake cloud, from discovery to transformation, deployment, and documentation.
How Can Data Be Transformed?
Data can be transformed in many ways, depending on the specific goals and requirements of the analysis. Here are some common methods for transforming data:
- Using mathematical functions
- Normalizing or standardizing data
- Aggregating or summarizing data
- Filtering data
- Joining data
- Parsing data
- Imputing missing data
- Encoding categorical data
What Are the 5 Stages of Transforming Data Into Information?
There are various models and frameworks for transforming data into information, but one common model is the Data-to-Information (D-I) process, which consists of five stages:
- Data collection
- Data processing
- Data analysis
- Information dissemination
- Decision-making
What Are the Three Forms of Data Transformation?
The three forms of data transformation are:
- Structure transformation
- Semantic transformation
- Data type transformation
What Is the Difference Between Data Transformation and Data Translation?
Data transformation focuses on changing the format or structure of the data, while data translation focuses on changing the language or terminology of the data. While these processes may sometimes overlap, they are distinct processes that serve different purposes in data management.
Why Do Data Transformation?
Data transformation is an important process in data management for several reasons:
- Improving data quality
- Facilitating data analysis
- Enabling data integration
- Supporting data visualization
- Enhancing data security
Conclusion
If your company is struggling to turn existing data into useful insights, data manipulation could be the solution. Of course, this requires selecting the appropriate type of data transformation method and knowing exactly what results you intend to achieve by transforming your data. Consulting data scientists can also assist you in developing a clear data manipulation plan.
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