December Lead Off Holiday Edition 🎄⚾

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Omni

What is it: Omni Analytics has launched Create Mode for its embedded analytics dashboard. This new feature lets users (with the right permissions, of course) build their own analyses and share them with peers within their organization. The idea? Empower users with self-service analytics and reduce the flood of data requests that usually bog down data teams.

Omni also shared upcoming features in its 2024 recap blog, and one stand out highlight is their focus on creating a more Excel-like interface. The goal is to make end users feel at home with the tool, lowering the learning curve and boosting adoption.

Why is it relevant: Tools like Omni Analytics and Sigma Computing are bringing an alternative take to the BI space by focusing on ease of adoption. Both use excel formula to SQL translation, and offer (or plan to offer) Excel-like frontends. For smaller businesses that don’t want to heavily invest in specialized BI resources, Omni could be a strong contender—just like Sigma.

Check everything out here.

Sigma Computing

What is it: Sigma computing has introduced writeback capabilities to AWS Redshift Data warehouse for input tables. “Input tables” in Sigma, give users flexibility to create custom tables through data entry, CSV uploads or modified tables built on top of existing ones. With Sigma’s spreadsheet-like interface, users can build these tables inside the tool, and push them directly to the data warehouse for broader use across their organization. This feature could be helpful for scenarios including budgeting analysis, custom groupings that shift annually, and team-specific rules.

Sigma did a winter product launch recently and has announced features including

• Ask Sigma which has a step by step explanation of AI responses generated,

• Custom level subtotals, & hierarchies for improved reporting, and

• Export bursts for push-based report subscriptions

You can read all about the product launch here.

Why is it relevant: Sigma is carving its niche by focusing on simplicity and familiarity with its spreadsheet-like interface. While it has some catching up to do compared to BI giants, its straightforward approach makes it an appealing option, especially for teams hesitant to adopt more complex BI tools.

Thoughtspot

What is it: Thoughtspot is rolling out the next iteration of Identity Access Management by layering Okta on its cloud service. This will allow organizations to setup Thoughtspot cloud apps more securely, by using MFA (multi-factor authentication), SSO (single sign on) support, and self-service management.

Why is it relevant: Safety is a big deal in the data world, and enhancements like this are always a welcome addition. Features like this which are meant to improve security, help moves the data industry forward, and improve trust across the board for BI and Data tools.

Read all about it here.

dbt

What is it: dbt Core, the open-source version of dbt, has introduced several long-awaited features in version 1.9 to enhance user experience and reduce the need for manual workarounds. One of the key additions is microbatch materialization for large datasets. This allows users to partition a large data table and process only a smaller portion at a time, saving on resources and time. There's also a retry option for failed batches, ensuring smoother operations when dealing with large-scale data.

Snapshots (used to store point-in-time versions of data for tracking changes) have been simplified in this update. The use of YAML for configuration makes setup more intuitive and organized. Additionally, users can now rename meta fields (which was previously impossible), and target-aware schemas are now an option.

There are other changes as well, including state:modified behaviors to generate less false positives by better accounting for configuration differences across environments. This update should make dbt better at falsely flagging any deliberate changes.

Read the entire release here.

AWS

What is it: One of the major announcements from AWS re:Invent was the launch of S3 Tables and S3 Metadata. With S3 Tables, AWS now offers a managed implementation of the Iceberg table format, which is increasingly popular for its performance when querying large datasets—think Netflix-scale data processing. On the other hand, S3 Metadata provides a managed solution for handling metadata, to manage and query data as it scales over time.

Read more about the feature here.

Why it matters: Wrapping a managed layer around an open source service improves accessibility of open source format, since they can be difficult for organizations to implement. With a managed layer, complexity is abstracted. However, that may come at a price, including vendor lock-ins. It is a tradeoff that needs to be considered when balancing ease of implementation versus freedom that comes with open source.

Data events action calendar 📅

• Snowflake RAG ‘n’ ROLL Hackathon is in Toronto on December 17th. Details here.

• Databricks Data Intelligence with Databricks on Google Cloud virtual workshop is on January 15, 2025. Details here.

• Google Cloud Next is happening in Las Vegas from April 9-11, 2025. Details here.

Cool Jobs in Data! 👩‍💻

• SickKids Hospital is hiring a VP, Business Intelligence and Analytics in Toronto

• Financeit is hiring a Director, Data Science & Reporting in Toronto

• OMERS is hiring a Data Engineer, Capital Markets in Toronto

• Airtable is hiring for multiple Data Scientist roles in Austin, TX; Los Angeles, CA; New York, NY; and San Francisco, CA — 1, 2 and 3

• Marriott is hiring a Senior VP, Data & Analytics in MD, US

• Instacart is hiring multiple Senior Data Engineers across US

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