Master Data Analytics with DOMO

Learn how to set up single sign on in Domo with an IdP provider, obtain client id and client secret, and enable password-free login via email.

Learn to connect to data sources and bring data into the Domo platform, using SQL Server, Google Sheets, Azure Data Lake Gen2 and Synapse, for ETL, transformation, and dashboards.

Explore the workbench interface, including the home status page, add account workflow, and the data set job interface for sending on-premise data to Domo.

Learn Domo Workbench scheduling by switching between local time and UTC, moving from manual to automated updates, and configuring basic and advanced frequencies, days, and monthly options.

Learn how to configure notifications in Domo Workbench, choosing notify on success or notify on error, saving settings, and adding user emails to receive success or failure alerts.

Create a batch file and schedule a Domo workbench job with Windows Task Scheduler. Run wb.exe with job id, set daily triggers, and review last run logs for data updates.

Connect a Google Sheet to Domo using the Google Sheet connector, paste the spreadsheet URL, select the sheet, specify data range, and set basic scheduling or manual runs.

Learn to connect azure synapse analytics with domo using the sql connector, configure server name, database, and credentials, adjust firewall settings, and run custom or builder queries to export data.

Connect Domo to Azure Data Lake Gen2 by supplying the account name and key, selecting the source container and product directory, then adjust delimiter settings for tab-delimited data.

Learn to connect Databricks to Domo with the Databricks connector, configure host and token, and import catalog data via a custom query from a SQL warehouse.

Explore connector action in Domo by reviewing update histories, using preview updated rows, and performing download, delete, and revert to this point to manage latest updates.

Bring data from SQL Server, Google sheet, file upload, data lake gen2, and Synapse serverless pool into Domo, then transform, join, and create cards and dashboards for retail analytics.

Explore cloud integration in Domo, comparing native and federated modes. Learn how data stays at the source, with transformations in Domo for native and visualization for federated.

Explore sql and etl data transformation in Domo, using sql or ui, covering date and number transformation, joins, unions, Python scripting, string and filter transformations, retail data analysis project.

Data blending in Domo combines datasets through inner or left joins and appends, designed for large data volumes to create views quickly across multiple datasets.

Explore how Domo's magic ETL enables extract, transform, and load for reporting. Learn the UI, data sets, and transformations, plus scheduling and performance options in magic ETL.

Learn how to apply the utility transformation in Domo to add a constant value to all rows by using add constraint, naming a flag with value zero.

learn to use add formula in a domo dataset to create new fields by applying date, string, and other functions, including case statements and quarter calculations.

Use the utility transformation to alter columns, rename product to product name and cost to cost price, drop state, then select columns for output.

Master the set column value transformation in Domo to replace a column with another of the same data type, such as text or fixed decimal.

Learn how the value mapper in Domo ETL modifies a product code column, replacing hardware with hard, with options to overwrite or create a new column and handle non-matches.

Apply the limit transformation in Domo Magic to restrict data rows by setting limit and offset, producing 100 records starting from a specified row.

Dynamically change a column's data type with meta select in Domo, converting date-like names such as birth date to date time using an add formula.

Apply a dynamic formula to decimal columns with the meta select transformation in Domo, automating round to two digits across new and existing fields via the schema and expression.

Learn how to perform a combined column transformation in Domo ETL by merging department and product code with a chosen separator, producing a new product department column.

Learn how to perform split column transformation in domo by choosing a source column, selecting a delimiter (space or hyphen), and using keep extra splits to capture all tokens.

Select a text column and apply replace text transformation in Domo to search and replace a term with a value. Explore options such as whole word, case sensitive, and regex.

Apply filter transformation with the add formula rule to customize ETL filtering, extracting the month from date and filtering for May, illustrating the greater flexibility of formula-based rules.

Learn how to perform unpivot in Domo, transforming multiple columns into a single column across multiple rows to normalize data within the ETL process.

Learn how dynamic unpivot in Domo converts multiple columns into a single value column, by designating non-pivoted columns and letting the remaining columns pivot automatically.

Explore joining in Domo ETL by configuring inner, left outer, right outer, and full outer joins on product and sale datasets, handling column conflicts with auto fix or rename.

Learn how to remove duplicates in a Domo transformation by selecting single or multiple columns to define uniqueness, keeping the first occurrence and outputting unique data.

Learn how the performance tile in Domo speeds ETL by using select and restore column to process only required columns, then restore the remaining columns for output.

Explore script activities in a Domo ETL workflow, compare Python and R options, and implement transformations with a Python script using pandas to filter cost greater than four in inputs.

Create a custom data set in Domo from scratch using the online spreadsheet editor, defining columns and entering values before saving.

Enable AI readiness on datasets, add AI-enabled columns, and provide context and synonyms to improve Domo AI accuracy.

Master data flow scheduling in Domo by choosing triggers, such as input data set updates or scheduled runs, and configuring frequency, days, times, and active hours for automated ETL execution.

Transform data with Domo to clean and enrich retail data by rounding prices, extracting date components, splitting product name and color, and computing final price and profit.

Explore SQL transformation in Domo, setting up data flows with customer and product datasets, enabling triggers and schedules, and understanding mode, privacy, and optimization options.

Learn to build intermediate data sets in Domo, perform SQL and other transformations, join data when possible, and apply updates to prepare input and output data sets.