Data Wrangling

TIBCO Spotfire®

Get guided insights, take action from AI-infused analytics and custom business applications.
Analyze data at-rest and data in-motion to predict trends via embedded data science and advanced analytics.

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Spotfire 12

The first step to gaining insight from analytics is ensuring organized, accurate, and actionable data. But studies show that 50–80% of analysis time is spent wrangling data that has errors, inconsistencies, and poor organization for analysis. Automated, inline data wrangling in TIBCO Spotfire® lets you connect, blend, clean, and wrangle data from any source including big data stores. Spotfire® generates automagical dataflows that record your steps on the data canvas, creating an auditable data lineage for easy editing, reuse, sharing, and scaling of analytics across your organization.

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Spotfire 12 Webinar Series: Introduction
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Spotfire 12 Webinar Series: Introduction

Capabilities

Fast and Easy Inline Data Wrangling

Inline data wrangling lets business users make adjustments: Mashup columns and rows from various data sources; unpivot with one click; change data type, category, and column name; dynamically group columns from visualizations; modify sort order; split smart columns; and cleanse data by replacing wrong or missing values. Full API support lets you add functions, like adding or changing join types to spark deeper insights.

Self-documenting Data Canvas

As you wrangle data, Spotfire automagically builds a data pipeline on the source view data canvas that documents all the changes made. Traceability and auditability of the data model are ensured with information about the data sources, connections, operations, and transformations automatically recorded. And the data model can be exported for reuse.

Turbocharged Recommendations Engine

Automatic recommendations for data structure and relationships in-memory and in-database save time. For example, recommendations might suggest adding table rows even before data is loaded; linking tables to configure instant shared marking; data loading options that make it easy to choose between in-database and in-memory tables; and categorization of in-database columns to make navigating big data a breeze.

Broad, Automated Data Access

With native connectors, easily connect to and blend data from relational and NoSQL databases; OLAP; Apache Drill, Hadoop, and Spark SQL; Impala; SAP HANA; and many others—and to cloud applications like Amazon Redshift, Databricks, RDS, Microsoft Azure SQL Database, Google Analytics, and Salesforce. Or easily build custom connectors. Natural Language Query lets you search for any data or connector.

Automation & Scalability

Spotfire Automation Services automates jobs like data loading, wrangling, transformation, and data and PDF export. Extensive APIs can trigger automations based on events. Spotfire Scheduled Updates lets you preload analyses into memory so teams can access them fast. Set uploads, especially large files, during off hours for efficient use of resources. Files load using smart memory sharing, serving tens of thousands of users with only a fraction of the memory otherwise required.

Benefits

Access and Link Any Data Source

Access drivers and data source documentation easily. Link multiple datasets. Smart column matching makes it possible to mashup data with one click.

Wrangle Data As You Go

Add, delete, and modify data tables and workflow steps as you analyze to save time. Choose between inline data wrangling or wrangling from the data canvas itself.

Edit Easily

Edit any of the recorded transformations you perform in the data canvas using classical agile drag-and-drop, NLQ search or AI-driven recommendations.

Scale Analytics

Scale to tens of thousands of users in any line of business—and reduce the resources burden through smart memory sharing. Shared workflows let teams ramp easily, with access to editable data wrangling steps they can adapt to their needs.

Cleanse, Prep, Analyze in One Tool

Simplify data prep and analytics. Stop moving back and forth between tools, which slows the process—and can introduce errors. Fix your data while you analyze for faster, more accurate results.

Configure, then Load

Eliminate waiting for each table to load before you make the next configuration. Wrangle inline, preview results based on configurable sampling, and view recommendations while data loads.