Data is growing at an exponential pace. Analysts predict that globally we will generate 97 zettabytes annually by 2023 – that‘s trillion gigabytes per year! However, raw unpublished data provides little value. The key is properly structuring and cleansing this tidal wave of information to drive innovation and gains through analytics. This process of refining messy, complex datasets for analysis is called data wrangling.
With the stakes so high, choosing the right data wrangling approach is critical. Start by quantifying your analytics objectives, data skills and infrastructure. For example, are business teams needing more self-service access or is IT looking to operationalize flows? This guide compares 5 leading solutions – friendly interfaces, coding languages and hybrid models – across key considerations. We‘ll uncover best fits based on use case and capability. You‘ll also get insider tips for maximizing platform value.
Let‘s get started and transform your data from liability to prime asset!
Why You Need Cutting Edge Data Wrangling
Very few organizations can afford the luxury of clean, consistent and comprehensive data. Instead analysts spend upwards of 80% of time just hunting down relevant information and massaging it. This crushes productivity and causes costly analytic mistakes from incorrect inputs. Modern data wrangling solutions provide a lifeline through automation, efficiencies and visibility.
The Wrangling Advantage
- Accelerated time-to-analysis by 10x as data scientists avoid grunt work
- Unified views from linking disparate datasets provide richer insights
- Higher quality data raises analytics and decision accuracy over 80%
- Enriched information better predicts emerging trends and future needs
- Automation makes processes reusable, scalable across data teams
Leading organizations leverage data wrangling platforms as a launchpad for success with bleeding edge techniques like AI/ML modeling. Let‘s examine top solutions tailor-made for the heavy lifting needed to tap analytics gold.
Key Data Wrangling Capabilities
Ideally your platform should excel across these core competencies:
Flexible data integration – seamlessly connect to APIs, databases, apps, unstructured files etc. Modern solutions erase technical barriers to ingest any data.
Scalable architecture – maintain interactivity while handling billions of records. Cloud-native platforms offer unlimited muscle.
Data discovery abilities – interactively profile statistics, drill down to spot contextual issues and identify relationships between elements. Makes root causes of problems clear.
Smart transformations – built-in routines for missing data, parsing strings, merging datasets etc. guided by machine intelligence to autogenerate best strategy.
Collaboration features – reuse existing data preparation flows. Adds consistency while eliminating redundant work. Governed access and sharing of prepared, documented assets.
Business friendly – intuitive visual interfaces, guided recommendations and configurable dashboards shield non-technical teams from complexity
Now let‘s analyze solutions that deliver on these aspects so your environment is primed for analytics success.
1. Talend Data Prep
Talend offers a unified data platform spanning integration, preparation, governance and machine learning. Data Prep delivers self-service wrangling to create analysis-ready datasets rapidly.
Key Advantages
- Unified data platform covering the entire pipeline
- Guided preparation of datasets using intuitive visual interface
- Ingest from diverse data sources – APis, files, apps
- Leverage machine learning for categorization, pattern recognition and bulk actions
- Managed sharing of prepared data, flows between teams
Talend handles the full lifecycle – access, understand, transform, govern, analyze and synchronize data. Core capabilities like data quality, metadata management and cataloging set up a solid foundation. Data Prep then simplifies reshaping information tailored to downstream needs while reusing wisdom. For example, you can automatically assign data types and patterns recognized from previous flows. Smart algorithms also recommend transformations based on statistical profiling. Teams seamlessly collaborate through packaged, documented datasets.
If your enterprise has fractured analytics stacks, Talend can help transform them into an efficient, governed ecosystem. The unified fabric knits together all the thread required for sustainable and smart analysis.
2. R Programming
This wildly popular open-source language has become ubiquitous for statistical computing and graphics. With over 10,000 community contributed packages, R delivers unparalleled flexibility for programming custom workflows. Whether your need is data imports, cleansing, visualization, predictive modeling or marshaling big data platforms, R provides the toolbox.
Key Advantages
- Thousands of specialized packages to extend functionality
- Handle data-intensive tasks on even the largest datasets
- Integrate analytics workflows with Spark, Python, Java, databases etc.
- Customize end-to-end machine learning pipelines for predictive modeling
- Community supported – packages continuously updated
R enables full control over your environment, shaped perfectly to analytical needs. The multitude of packages helps tackle new challenges without starting from scratch. R also parallelizes across systems for fast response times even on billions of records. Integration with other languages provides an execution bridge across environments. Data engineers harness R for ETL while scientists apply its renowned visualization and predictive abilities.
While coding expertise is required, R skills also transfer well across organizations compared to proprietary tools. Resources abound for learning via the vibrant community constantly pushing boundaries. Mastering R unlocks unlimited potential for customization well beyond predefined platforms.
3. Trifacta Enterprise Data Engineering
Trifacta focuses squarely on making self-service data wrangling easy and scalable. Its Photon and Smart Data Hub engines examine dataset characteristics and guide interactive cleansing through machine learning. Users directly manipulate visual profiles instead of coding complex logic.
Key Advantages
- Visually select columns and define data quality rules
- Interactive suggestions to transform data using ML techniques
- Build end-to-end workflows with data dependencies
- Integrates natively with cloud data platforms
- Scales to process billions of records in parallel
Trifacta makes transformation logic transparent yet hidden. It learns dataset properties while empowering direct manipulation using intuitive visual constructs. Users set data quality standards, identify violations then iteratively resolve guided by suggestions. This builds understanding while accelerating development. Workflows package together sequences for reapplication across batches with inherited logic.
Trifacta delivers extreme automation so analysts spend less time preparing and more time unlocking insights. It is cloud-native, securing and scaling access across the enterprise. Trifacta excels at empowering a blend of skill sets to wrangle diverse datasets faster.
4. OpenRefine
This leading open source solution has earned renown for tackling messy, inconsistent data quickly through clustering algorithms. It can instantly profile and manipulate large datasets stored locally or remotely.
Key Advantages
- Rapidly explore inconsistencies using column facets
- Develop robust transformations reusable across projects
- Extend capabilities using expressions and external services
- Fully customize instance on local machine or cloud
- Export analysis-ready structured dataset
OpenRefine brings an intuitive yet powerful toolkit to overcome data obstacles. Unique column facets accelerate exploring variances across categories, geographies etc. Inspiration often strikes to overcome hurdles just browsing the visual interface! Standard transformations combine, split, nest and filter data skillfully. Extensions and APIs maximize possibilities working with external libraries.
While best suited for technically adept users, OpenRefine delivers extreme capability honed over years of community feedback. The expressive power condones shallower learning curves making this an ideal addition to any stack. Being open source also ensures no vendor lock-in.
5. Datameer
Datameer offers a governed data preparation solution focused on flexibility, control and reuse across teams. It provides a code-optional paradigm with robust auditability. Datameer also natively supports leading cloud platforms.
Key Advantages
- Code-free preparation secured by governance guardrails
- Reusable data pipelines with shared business logic
- Native connectors for cloud platforms like Snowflake
- Extend transformations using Python, Java, Spark
- Fine-grained access controls and encryption
Core differentiators include hybrid interfaces, collaboration and trust enablement. Business users leverage guided preparation while engineers script complex logic. All workartifacts – datasets, flows, models, join logic etc. – become documented enterprise assets. Standards like encryption, access controls and logging help meet compliance demands. Integration with IT managed cloud platforms closes self-service gaps around security, automation and scaling.
Datameer empowers decentralized teams to wrangle data tailored to their skillset while keeping IT needs front and center. This helps churn value quickly without compromising governance or reuse.
Smart Selection Guide
| Tool | Learning Curve | Infrastructure | Data Volumes | Flexibility | Visual Interface | Collaboration |
|---|---|---|---|---|---|---|
| Talend | Low | On-prem/Cloud | Billions of rows | Low code customization | Excellent | Shared workspaces and flows |
| R | Steep | Any environment | Extreme big data | Maximum via packages/languages | Programmatic | Version control integration |
| Trifacta | Low | Cloud data platform | Billions of rows | Low code customization | Excellent | Share flows and outputs |
| OpenRefine | Medium | Desktop install | Millions of rows | Highly extensible via APIs | Good for profiling | Export structured outputs |
| Datameer | Medium | Cloud data platform | Billions of rows | Hybrid code/low-code | Good for profiling | Metadata sharing, reviews |
Key Selection Criteria
Consider these aspects before committing to a solution:
Skill level – Low/no code platforms like Talend, Trifacta if business teams need self-service. R if deep analytical programming skills exist.
Infrastructure – Cloud native options like Trifacta best utilize managed warehouses. Otherwise maximize existing investment.
Data scale/complexity – Prioritize robust technology like Talend, Spark if extremely large or intricate.
Agility needs – Prefer Open Source platforms like R, OpenRefine if schemas frequently evolve.
Compliance needs – Solutions like Datameer ensure governance, access controls for sensitive data.
Team collaboration – Reuse, standardization maximize value. Talend, Datameer enable this through shared flows.
Emerging Capabilities to Watch
Data wrangling solutions will continue maturing at a rapid pace by assimilating latest technology advancements:
Expanding access – AutoML, conversational interfaces via natural language and virtual assistants will lower skill barriers
Increased automation – AI/ML assisted data assessments and profiling will accelerate creation of ready-to-use datasets
Big data integration – Direct connectivity for data lake sources like Spark, big query handles web scale messy information
Multi-cloud portability – Environment agnostic data preparation reusable across leading cloud platforms
Embedded customization – Expansive APIs and scripting languages provide nuanced control within guided interfaces
Wrap Up
Data wrangling forms the sturdy foundation on which analytics excellence is built. Modern solutions help enterprises across industries turn raw information into their most vital asset. Approach platform evaluation holistically based on use case, team priorities and infrastructure. Mix low code interfaces for usability with customization options where needed. This balances simplicity with flexibility to shape perfect solutions. Stay current on advances in cloud platforms, machine learning and visualization to maximize value. Following best practices will reward you with the trusted, accessible and actionable data volume that separates leaders from laggards.
Hope this guide sparks ideas that help your organization thrive with analytics. Share your experience or feedback on the latest innovations. The collective wisdom we gain speeds up data‘s transformation from messy liability to indispensable asset!
