Solutions Review’s listing of the best data wrangling tools and software is an annual mashup of products that best represent current market conditions, according to the crowd. Our editors selected the best data wrangling tools based on each solution’s Authority Score; a meta-analysis of real user sentiment through the web’s most trusted business software review sites and our own proprietary five-point inclusion criteria.
The editors at Solutions Review have developed this resource to assist buyers in search of the best data wrangling tools to fit the needs of their organization. Choosing the right vendor and solution can be a complicated process — one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we’ve profiled the best data wrangling tools providers all in one place. We’ve also included platform and product line names and introductory software tutorials straight from the source so you can see each solution in action.
Note: The best data wrangling tools are listed in alphabetical order.
The Best Data Wrangling Tools
Platform: Altair Monarch
Related products: Altair Knowledge Hub
Description: Altair Monarch is a desktop-based self-service data preparation tool that can connect to multiple data sources including unstructured, cloud-based and big data. Connecting to data, cleansing and manipulation tasks require no coding. The tool features more than 80 pre-built data preparation functions, and models built within the product can be exported into common BI or other analytics platforms. Altair Knowledge Hub is browser-based that provides visual-based data preparation and machine learning to suggest data enrichment and transformation during the data preparation process.
Platform: Alteryx Designer
Related products: Alteryx Analytics and Data Science Platform
Description: Alteryx Designer is a part of the company’s flagship analytics and data science platform. The tool features an intuitive user interface that enables users to connect and cleanse data from data warehouses, cloud applications, spreadsheets, and other sources. Users can leverage data quality, integration and transformation features as well. Alteryx Designer also includes data blending for spatial data files so they can be joined with third-party data such as demographics.
Description: Cambridge Semantics offers a data discovery and integration platform called Anzo that lets users find, connect and blend data. Anzo connects to both internal and external data sources including cloud or on-prem data lakes. The product also features data cataloging that utilizes graph models encoding a Semantic Layer that describes data in business context. Users can add Data Layers for data cleansing, transformation, semantic model alignment, relationship linking, and access control as well.
Platform: Datameer Enterprise
Related products: Datameer X
Description: Datameer offers a data analytics lifecycle and engineering platform that covers ingestion, data preparation, exploration and consumption. The product features more than 70 source connectors to ingest structured, semi-structured and unstructured data. Users can directly upload data or use unique data links to pull data on demand. Datameer’s intuitive and interactive spreadsheet-style interface lets you transform, blend and enrich complex data toward the creation of data pipelines.
Platform: Infogix Data360 Analyze
Description: Infogix offers a suite of integrated data governance capabilities that include business glossaries, data cataloging, data lineage, and metadata management. The tool also provides customizable dashboards and zero-code workflows that adapt as each organizational data capability matures. Organizations use Infogix for data governance and for risk, compliance and data value management. The product is also flexible and easy to use, and supports smaller data analysis jobs as well.
Platform: Paxata Self-Service Data Preparation
Description: Paxata Self-Service Data Preparation is an application within its Adaptive Information Platform. The product features flexible deployment and self-service operation. The app is built on a visual user interface that has familiar spreadsheet metaphors so users don’t have to learn an entirely new tool. The app also boasts Assisted Intelligence that provides algorithmic assistance to infer the meaning of data, and machine learning captures steps for future data work.
Platform: Trifacta Wrangler
Related products: Trifacta Wrangler Pro, Trifacta Wrangler Enterprise, Google Cloud Dataprep by Trifacta
Description: Trifacta offers a suite of what its dubbed ‘data wrangling’ tools in three different iterations: Trifacta Wrangler, Wrangler Edge, and Wrangler Enterprise. Trifacta allows users to do data prep without having to manually write code or use mapping-based systems. The Predictive Transformation function enables the exploration of data content so users can define a recipe for how the data should be transformed. Data Wrangler also includes data discovery, structuring, cleaning, enriching, and validation capabilities.
Platform: Talend Data Preparation
Description: Talend Data Preparation utilizes machine learning algorithms for standardization, cleansing, pattern recognition and reconciliation. The product also provides automated recommendations to guide users through the data preparation process. Talend provides governance via role-based access, masking rules, and workflow-based data curation. Users can share preparations and datasets or embed data preparations into bulk, batch, and live data integration as well.
Platform: Tamr Unify
Description: Tamr offers a machine learning-based data integration product called Unify. The solution allows organizations to connect to any tabular data and publish it anywhere. Users can map schemas with machine learning suggestions and normalize data formats using Spark and SQL. Tamr’s Master Records feature provides a complete view of all entities via simple yes and no questions as well. The company was originally invented by Dr. Michael Stonebraker and his colleagues who published their research about the Data Tamer System for handling large-scale data curation in 2013.
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