There exist terabytes and petabytes of data in this Internet age, with exponential growth on the same. But how do we consume this data and translate it to helpful information to improve service availability?
Valid, novel and understandable data is all businesses need for their knowledge discovery models.
For this reason, businesses are applying analytics in many different ways to uncover quality data.
But where does it all start? The answer is data wrangling.
Let’s get started!
What Is Data Wrangling?
Data wrangling is the act of cleaning, structuring, and transforming raw data into formats that simplify data analytics processes. Data wrangling often involves working with messy and complex data sets that are not ready for data pipeline processes. Data wrangling moves raw data to a refined state or refined data to optimized state and production-ready level.
Some of the known tasks in data wrangling include:
- Merging multiple datasets into one large dataset for analysis.
- Examining missing/gaps in data.
- Removing outliers or anomalies in datasets.
- Standardizing inputs.
The large data stores involved in data wrangling processes are usually beyond manual tuning, necessitating automated data preparation methods to produce more accurate and quality data.
Goals of Data Wrangling
Besides preparing data for analysis as the bigger goal, other goals include:
- Creating valid and novel data out of messy data to drive decision-making in businesses.
- Standardizing raw data into formats that Big Data systems can ingest.
- Reducing the time spent by data analysts when creating data models by presenting orderly data.
- Creating consistency, completeness, usability, and security for any dataset consumed or stored in a data warehouse.
Common approaches to Data Wrangling
Before data engineers start data preparation tasks, they need to understand how it is stored, the size, what records are kept, the encoding formats, and other attributes describing any dataset.
This process involves organizing data to take readily usable formats. Raw datasets may need structuring in how the columns appear, the number of rows, and tuning other data attributes to simplify analysis.
Structured datasets need to be gotten rid of inherent errors and anything that can skew the data within. Cleaning thus entails removing multiple cell entries with similar data, deleting empty cells and outlier data, standardizing inputs, renaming confusing attributes, and more.
Once data has passed the structuring and cleaning stages, it is necessary to assess data utility and augment it with values from other datasets lacking to give the desired data quality.
The validating process entails iterative programming aspects that shed light on data quality, consistency, usability, and security. Validating phase ensures all transformation tasks are achieved and flags datasets as ready for analytics and modeling phases.
After all the stages are passed, the wrangled datasets are presented/shared within an organization for analytics. Documentation of preparation steps and metadata generated along the wrangling process is also shared in this stage.
Talend is a unified data management platform wrapped in 3 data fabrics to provide reliable and healthy data. Talend presents Data integration, Application and Integration, and Data Integrity & Governance. Data wrangling in Talend is through a browser-based point and click tool that allows batch, bulk, and live data preparations – data profiling, cleaning, and documentation.
Talend data fabric handles every stage of the data lifecycle, carefully balancing data availability, usability, security, and integrity of every business data.
Were you ever worried about your diverse data sources? Talend’s unified approach provides rapid data integration from all your data sources (databases, cloud storages, and API endpoints) – allowing transformation and mapping for all data with seamless quality checks.
Data integration in Talend is enabled through self-service tools such as connectors that allow developers to ingest data from any source automatically and adequately categorize the data.
Features of Talend
Universal data integration
Talend allows businesses to wrangle any data type from varied data sources – Cloud or On-prem environments.
Talend goes beyond vendor or platform when building data pipelines out of your integrated data. Once you create data pipelines out of your ingested data, Talend allows you to run the pipelines anywhere.
With machine learning capabilities such as data deduplication, validation, and standardization, Talend cleanses ingested data automatically.
Support for Application and API integrations
After meaning is made out of your data through the Talend self-service tools, you can share your data through user-friendly APIs. Talend API endpoints can expose your data assets to SaaS, JSON, AVRO, and B2B platforms through advanced data mapping and transformation tools.
R is a well-developed and effective programming language to tackle exploratory data analysis for scientific and business applications.
Built as free software for statistical computing and graphics, R is both a language and environment for data wrangling, modeling, and visualization. The R environment provides a suite of software packages while R language integrates a series of statistical, clustering, classification, analysis, and graphical techniques that help manipulate data.
Features of R
Rich Set of Packages
Data engineers have more than 10,000 standardized packages and extensions to select from the Comprehensive R Archive Network (CRAN). This simplifies wrangling and data analysis.
With distributed computing packages available, R can perform complex and straightforward manipulations (mathematical and statistical) on data objects and datasets within a matter of seconds.
R is platform-independent, capable of running on many Operating Systems. It is also compatible with other programming languages that help in manipulating computationally heavy tasks.
Learning R is easy.
Trifacta is an interactive cloud environment for profiling data that are run against machine learning and analytics models. This data engineering tool aims to create understandable data regardless of how messy or complex the datasets are. Users can remove double entries and fill blank cells in datasets through deduplication and linear transformation transformations.
This data wrangling tool has an eye for outliers and invalid data in any dataset. With just a click and drag, data at hand is ranked and intelligently transformed using suggestions powered by Machine learning to accelerate data preparation.
Data wrangling in Trifacta is through compelling visual profiles that can accommodate non-technical and technical staff. With the visualized and intelligent transformations, Trifacta prides itself in its design for users in mind.
Whether ingesting data from data marts, data warehouses, or data lakes, users are shielded from the complexities of data preparations.
Features of Trifacta
Seamless Cloud integrations
Supports preparation workloads across any cloud or hybrid environment to allow developers to ingest datasets for wrangling no matter where they live.
Multiple data Standardization methods
Trifacta wrangler has several mechanisms for identifying patterns in data and standardizing the outputs. Data engineers can choose standardization by pattern, by function, or mix and match.
Trifacta organizes data preparation works in the form of flows. A flow contains one or more datasets plus their associated recipes (defined steps that transform data).
A flow, therefore, reduces the time developers spend when importing, wrangling, profiling, and exporting data.
OpenRefine is a mature, open-source tool for working with messy data. As a data cleaning tool, OpenRefine explores datasets in a matter of seconds while applying complex cell transformations to present desired data formats.
OpenRefine approaches data wrangling through filters and partitions on datasets using regular expressions. Using in-built General Refine Expression Language, data engineers can learn and view data using facets, filters, and sorting techniques before performing advanced data operations for entity extractions.
OpenRefine allows users to work on data as projects where datasets from multiple computer files, web URLs, and databases can be pulled into such projects with the ability to run locally on users’ machines.
Through expressions, developers can extend data cleanup and transformation to tasks like splitting/joining multi-valued cells, customizing facets, and fetching data into columns using external URLs.
Features of OpenRefine
OpenRefine is built to work with Windows, Mac, and Linux operating systems through downloadable installer setups.
Rich set of APIs
Features OpenRefine API, data extension API, reconciliation API, and other APIs that support users’ interaction with data.
Datameer is a SaaS data transformation tool built to simplify data munging and integration through software engineering processes. Datameer enables extraction, transformation, and loading of datasets to Cloud data-warehouses such as Snowflake.
This data wrangling tool works well with standard datasets formats such as CSV and JSON, allowing engineers to import data in varied formats for aggregation.
Datameer features catalog-like data documentation, deep data profiling, and discovery to meet all data transformation needs. The tool keeps a deep visual data profile that allows users to trace invalid, missing, or outlying fields and values and the overall shape of data.
Running on a scalable data warehouse, Datameer transforms data for meaningful analytics through efficient data stacks and excel-like functions.
Datameer presents a hybrid, code, and no-code user interface to accommodate broad data analytics teams who can build complex ETL pipelines easily.
Features of Datameer
Multiple user environments
Features multi-person data transformation environments – low code, code, and hybrid, to support tech-savvy and non-tech persons.
Datameer allows teams to reuse and collaborate on models to speed projects.
Rich data documentation
Datameer supports both system and user-generated data documentation through metadata and wiki-style descriptions, tags, and comments.
Final Words 👩🏫
Data Analytics is a complex process, which requires the data to be appropriately organized to draw meaningful inferences and make predictions. Data Wrangling tools help you format large amounts of raw data to help you perform advanced analytics. Choose the best tool that suits your requirement and become an Analytics pro!
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