Elementary

Going to Big Data London? Make sure to stop by and say hi to the Elementary team ?? . Maayan Salom, Stas Michalski, Itamar Hartstein, and Daniel Pollak will be there, and they would love to hear about your experience with Elementary and show you some of the features we recently introduced on the Elementary Cloud platform. Book time with the team here: https://lnkd.in/dW2Hp3SP

Is data health on your radar yet? It should be ???? Next Wednesday, join Maayan Salom and Or Avidov as they sit down with Shenhav Lavie and Oren Sarfaty from Fiverr to dive deep into data health — why it matters and how to measure it. They'll also reveal the inside story of building Fiverr's new data platform and why data quality was top priority in every step of the way. Don’t miss out — sign up here: https://lnkd.in/drSZSVAn

???? The Elementary team is coming to London! ???? We're hosting a meetup on September 17th, and we’d love to see you there. What’s on the agenda? How to measure data quality and share quality scores with consumers. Check out the full agenda and sign up here: https://lnkd.in/dsXbdM5C

You don’t want to miss this one! ?? Shenhav Lavie, Senior Director of Data Development at Fiverr, and Oren Sarfaty, Data Developer at Fiverr, will share their journey of rebuilding Fiverr's data platform. They will discuss the organizational challenges related to data quality, how they solve them, and what they plan for in the future. Topics covered in this session: - Fiverr's journey of rebuilding their data platform - The challenge of measuring data quality - How to measure data quality and share quality scores with consumers and stakeholders - Tracking and communicating progress in improving data quality over time Make sure to save your seat! ?? https://lnkd.in/drSZSVAn

dbt artifacts provide valuable insights into your dbt project. They are useful for data observability, documentation, debugging, analytics and compliance. While you can manage and store dbt artifacts yourself, using Elementary can simplify the process and provide additional features like automated alerts, data observability dashboards, and end-to-end lineage visualization. Alex Alves created a comprehensive guide explaining how you can make the most out of dbt artifacts. Check it out here: https://lnkd.in/dNpBVdFH

Introducing Automated Anomaly Detection! ?? Elementary Cloud now offers out-of-the-box monitoring for freshness and volume issues across all production tables, ensuring comprehensive detection of critical pipeline issues without any configuration effort or access to raw data. - Monitors track table updates and detect data delays, incomplete updates, and significant volume changes. - No increase in compute costs, as monitoring leverages only warehouse metadata. - Instant operation with no training period; models run on collected metadata immediately after initial backfill. - Native integrations with major data warehouses, including Snowflake, BigQuery, Databricks, and Redshift. Read more on our docs: https://lnkd.in/dMk63c9q

Introducing: Data health scoring! ?? Recognizing the challenges many users face in scoring the health of public tables, we've developed a new feature that simplifies this critical task. Our new Data Health Dashboard lets you communicate a data set's health state to users who are less familiar with specific tests and validations and need to know whether a data set is usable. The health of each data set is calculated based on 6 dimensions: - Accuracy - Consistency - Completeness - Freshness - Uniqueness - Validity To easily improve coverage, the dashboard instantly highlights any dimensions lacking testing. You can easily share the dashboard with team members—granting view-only access if needed. ?? Read more on our docs: https://lnkd.in/des8y85K

Data Reliability in Real Life - I recently had a blood test where one result was out of the normal range. My doctor, whom I trust completely, advised me to consult a specialist about potential medical treatment. As I had no symptoms, this seemed suspicious. I asked my doctor for another test, but she said nothing would change in such a short timeframe. While I trust my doctor, I know mistakes happen, and data is reliable only when validated. I decided to take two additional tests the following week, several days apart. I still consulted the specialist, but this time I had data to suggest the outlier result should be taken with a grain of salt. Last year, my dad received a test result stating he had a "healthy uterus." Data reliability issues can occur anywhere. Data validation is key for making the right decisions. PS - I love that my health provider lets me create cool graphs :)

Building a reliable data platform depends on having comprehensive test coverage, which is a major challenge for many of our users. We've noticed a significant gap: users don't know which tests are available and which ones are right for their use case, so we created the dbt Test Hub What's included in the hub? - Test Catalog: Documentation of the various tests provided by different dbt packages - Use Cases: Segmentation by use case, and descriptions of the scenarios each test is best suited for - Detailed Functionality: How each test works - Usage Examples: To help you implement tests more effectively Give it a try here:https://lnkd.in/dwfsfWdN Big thanks to Alex Alves, who helped us create this amazing catalog. P.S. All the tests in the Hub are available in the?Elementary Cloud Platform under?the test configuration section. This enables you to add tests in bulk to multiple columns/tables, by opening a PR in your name from the Elementary UI. Check it out here: https://lnkd.in/db6JsqmJ