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1.1 Data Connections: Live Connections vs. Hyper Extracts
Salesforce Certified Tableau Data Analyst Exam
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1 Chapter 1: Connecting to Data & The Logical Data Model
1.1 Data Connections: Live Connections vs. Hyper Extracts 1.2 The Tableau Data Model: Relationships vs. Physical Joins 1.3 Combining Data: Data Blending vs. Cross-Database Joins
2 Chapter 2: Tableau Prep Builder & Data Wrangling
2.1 Tableau Prep Architecture & Data Cleaning 2.2 Reshaping Data in Prep: Pivots, Splits & Wildcard Unions 2.3 Joins, Aggregations & Output Destinations in Prep
3 Chapter 3: Data Quality, Metadata & Field Configuration
3.1 Data Types, Roles, Organization & Default Properties 3.2 In-Desktop Data Cleaning: Splits, Data Interpreter & Source Replacement 3.3 Managing Slowly Changing Dimensions & Data Quality Warnings
4 Chapter 4: Tableau Fundamentals & Calculations
4.1 Dimensions vs. Measures & Discrete vs. Continuous 4.2 Mastering Dates: Discrete Date Parts vs. Continuous Date Truncations 4.3 Calculations: Logic, Strings, Nulls & Type Conversion 4.4 Aggregation Rules, Granularity & Dealing with the Asterisk (ATTR)
5 Chapter 5: Level of Detail (LOD) Expressions
5.1 Level of Detail Architecture & Scoping Syntax 5.2 FIXED LOD Expressions: Scoping & Baseline Benchmarks 5.3 INCLUDE & EXCLUDE LOD Expressions: Dynamic Granularity 5.4 Real-World LOD Patterns: Cohorts, First/Last Dates & Binning
6 Chapter 6: Table Calculations & Directionality
6.1 Table Calculation Mechanics: Addressing vs. Partitioning 6.2 Essential Table Calculations: Running, Window & Percent of Total 6.3 Navigation & Ranking Functions: LOOKUP, INDEX & RANK 6.4 Secondary & Nested Table Calculations: Multi-Level Scoping
7 Chapter 7: Statistical Analytics, Filtering & Order of Operations
7.1 The Tableau Order of Operations: From Extract to Display Filters 7.2 Sets, Set Actions & Combined Sets vs. Groups 7.3 Parameters, Parameter Actions & Dynamic What-If Analysis 7.4 Analytics Pane: Totals, References, Trends & Forecasts
8 Chapter 8: Chart Types & Comparative Visualizations
8.1 Comparison Visualizations: Bar Charts, Highlight Tables & Bullet Graphs 8.2 Part-to-Whole & Correlation Charts: Treemaps, Scatter Plots & Bubbles 8.3 Trend & Distribution Visualizations: Dual-Axis, Combos & Histograms
9 Chapter 9: Advanced Visualizations & Spatial Analysis
9.1 Specialized Visualizations: Waterfall, Gantt, Sparklines & Bump Charts 9.2 Spatial Data & Mapping: Geographic Layers & Spatial Functions 9.3 Visual Polish: Custom Palettes, Annotations & Viz in Tooltip
10 Chapter 10: Dashboard Design & Interactive Actions
10.1 Dashboard Layout, Devices & Visibility 10.2 Filter, Highlight & URL Actions 10.3 Advanced Interactivity: Parameter, Set & Navigation Actions 10.4 Stories & Visual Storytelling: Sequencing Analytical Narratives
11 Chapter 11: Tableau Server & Cloud: Architecture & Publishing
11.1 Tableau Server & Cloud: Sites, Projects, Roles & Permissions 11.2 Publishing Workbooks, Data Sources & Flows
12 Chapter 12: Data Governance, Security & Operations
12.1 Certification, Data Quality, Lineage & Permissions 12.2 Row-Level Security, Schedules, Subscriptions & Alerts
1.1 Data Connections: Live Connections vs. Hyper Extracts
Key Takeaways Explain This Topic AI
Live connections query the source for analytical interactions; extracts query a refreshed .hyper snapshot.
A full refresh reconciles the complete extract, while incremental behavior depends on the identifying key and any configured replacement range.
Current advanced incremental options can replace the highest non-unique key group or a recent date subrange, but older changes may still require a full refresh.
Custom SQL defines rows and can constrain Tableau optimizations, but the database still controls its execution plan and indexes.
Initial SQL runs connection-establishment commands and is distinct from Custom SQL.
Last updated: September 2026
1.1 Data Connections: Live Connections vs. Hyper Extracts
The first design decision in Tableau is choosing the source and the connection mode. Start with the analytical requirement, not a preference for a particular connector. Confirm where the authoritative data lives, how current the view must be, how much data is needed, whether users need offline access, what authentication the source requires, and whether the source supports the Tableau features you intend to use. Tableau can connect to files, relational and cloud databases, and published data sources. Replacing a source later is possible, but matching field names and data types reduces broken references.
Live connections
A live connection leaves the detailed records in the source. Tableau translates a view into queries that the source executes, and the returned results become marks. A new filter, drill, or sort can cause new queries. This makes live a strong choice when users need current source data or when database policies and user-specific credentials must govern every request. It also means performance depends on the database, network, query complexity, concurrency, and source availability.
Live does not mean that every dashboard is automatically fast or that Tableau stores no temporary information at all. It means the governed analytical data is queried from the source rather than refreshed into a Tableau extract. Reduce unnecessary fields and marks, test representative filters, and use the database's own monitoring tools when diagnosing a slow live workbook. A warehouse built for analytical concurrency is a better live candidate than an overloaded transactional application database.
Hyper extracts
An extract is a persisted snapshot stored in Tableau's .hyper format. Tableau queries the extract for the view instead of repeatedly querying the original source. Extracts are useful for portable packaged workbooks, sources with limited analytical performance, and dashboards that can tolerate refresh-based freshness. Extract filters can limit rows before they enter the extract. Hiding unused fields and aggregating data for visible dimensions can also reduce the extract, but aggregation removes detail that later analysis might need.
An extract is not automatically the right answer for every large source. Its refresh duration, storage, security design, and freshness service level must be acceptable. Likewise, live is not automatically fresher if a source itself loads only once per day. Compare the complete data pipeline.
- Decision factor Live Extract
- Freshness Reflects source state when queried Reflects the last successful refresh
- Query workload Sent to the source Served from the Hyper extract
- Offline use Usually requires source access Can support portable packaged workbooks
Source functions Limited by connector and source capabilities Evaluated with extract-supported Tableau behavior
- Operations Monitor database and credentials Monitor refreshes, storage, and credentials
- Full and incremental refreshes
A full refresh rebuilds the extract and is the reliable way to reconcile inserts, updates, and deletions. A basic incremental refresh uses an identifying column such as an increasing ID or timestamp and retrieves values after the stored maximum. That approach can miss an old row whose non-key attributes changed and can retain a source row that was deleted.
Current Tableau releases also provide advanced incremental options. With a non-unique identifying key, Tableau can replace the group of rows sharing the current highest key before adding newer groups. With a date or date-time key, a configurable subrange can be re-extracted so recent corrections are replaced. These options improve common cases, but neither turns an incremental refresh into a universal change-data-capture system. A correction outside the replacement group or date subrange still requires a full refresh or a source design that exposes changes through a suitable key. Test the chosen policy with late-arriving, updated, and deleted records.
Source and extract filters
A data source filter restricts data available to every worksheet using that data source. An extract filter restricts rows copied into an extract. Apply an extract filter before creating or refreshing an extract when the excluded rows should not be stored. A worksheet filter acts later and ordinarily affects only its sheet unless its scope is expanded. Do not treat a worksheet filter as a substitute for source-level security: viewers with sufficient download or authoring capabilities might reach data that merely was not shown in one view.
Custom SQL and Initial SQL
Custom SQL defines a logical table from a statement you supply. It is appropriate when the required source-side logic cannot be expressed conveniently with Tableau's data model and a governed database view is unavailable. Tableau normally treats the statement as a query and may wrap it when it generates later analytical queries. That can limit Tableau's ability to prune fields or tables and can make source troubleshooting harder. The source database still chooses its execution plan and can still use indexes; Custom SQL does not inherently disable database indexing. Measure performance rather than assuming every Custom SQL statement is slow.
Initial SQL serves a different purpose. For supported connectors, it runs commands when the connection is established—for example, setting a session variable or initializing a temporary object. It does not itself define the rows shown on the Data Source page. Because connection pooling and reconnects can cause it to run more than once across a session, Initial SQL should be safe to execute whenever Tableau opens the connection.
Practical selection workflow
Identify the authoritative source and required tables or published source.
Confirm authentication, network access, connector support, and source policies.
Choose live when current data and source-enforced behavior outweigh query-load concerns.
Choose an extract when refresh-based freshness, portability, or analytical offload is preferable.
Apply only justified source or extract filters and retain required detail.
For incremental refreshes, document which updates and deletions can be missed and when a full refresh will reconcile them.
Validate row counts, key totals, null rates, and representative dashboard results after connection or source replacement.
The defensible answer in a scenario is the connection that satisfies freshness, governance, scale, and operational constraints together. Neither connection mode is universally superior.