Visible Alpha’s Post

Once you have a data contract defined you can represent it in as many different ways as you want. That could include: - Protobuf, to configure a Kafka topic with a schema - JSONSchema, to create client libraries for data validation before it leaves the publishing service - Custom JSON or YAML to integrate with any number of tools and services such as data catalogs, cloud resources, and so on Being both human and machine readable, the data contract is then the source of truth for everything about the data, and that truth is replicated as needed, usually by writing trivial code to transform it to a different format. One day there could be a widely adopted open standard that replaces the need to create so many custom data structures (👋 Bitol!). But until then it will be common to transform the data contract into many different representations. #DataContract #DataQuality

Common feedback from clients is the lack of clear outcomes and timelines with transparent pricing. FUEL from Pandata Group addresses that for organizations who are looking to implement Snowflake. If you're considering Snowflake and want to learn more about how we can make the transition easier, please reach out!

Accelerate your platform implementation of the Snowflake Data Cloud. ❄ FUEL is built to cover these 4️⃣ milestones in 5️⃣ five weeks. 1. Discovery: Select sample data sources to replicate in Snowflake, decide on queries for visualization, and assess monitoring, logging, and auditing requirements. 2. Implementation: Build out a data platform infrastructure, a data pipeline to process your data, and create a dashboard in the tool of your choice. 3. Validation: User Acceptance Testing is conducted to validate the functionality of the data pipeline. 4. Project Handoff: Presentation of deliverables with proper enablement to transfer ownership of the platform assets. Explore more ➡ https://lnkd.in/gbqwTjx8

This has been a flagship service offering for several years to identify the quick-win, show value, and set the data infrastructure up for security, governance, efficiency, and scale. If you’re starting your data journey - this is how. If you’re already on Snowflake - get ready for Fuel AI for Snowflake.

📊 New in Swarmia: You can now export data from Swarmia and bring it to your data warehouse, whether that’s in Snowflake, BigQuery, or Redshift. Read more in the changelog.

Our releem partner announced a new feature, "SQL Query Analytics." What Can You Do With Query Analytics? 1. Identify slow queries 2. Identify high-impact queries 3. Inspect query details How Query Analytics Works With a simple click of "Load Data," Releem Cloud Platform initiates a task for Releem Agent to gather data from MySQL Performance Schema for analysis. Once collected, you can view the data on your top 100 queries directly within the dashboard. Make your life easier with Releem

Loading data from various sources into your cloud platform has never been easier. Matillion Data Loader provides a streamlined, user-friendly experience that allows you to connect to multiple data sources, transform the data as needed, and keep your data pipelines running smoothly. Whether you're dealing with complex datasets or simply need a reliable ETL solution, Matillion has you covered. Ready to get started? Visit our blog: https://hubs.la/Q02LRsv70 #Matillion #DataLoader #ETL #CloudData

Have you ever wanted to know what the easiest way to integrate Snowflake Tasks and Airbyte Cloud jobs is? 🤓 We'll give you some ideas for what is isn't: 👉 Airflow 👉 Another open-source workflow orchestration package 👉 A native connector (although where Snowflake offers 0 ELT, you should totally take it) It is: ✅ No-code / low-code ✅ Can be set up in minutes ✅ Currently Free ✅ Has Kick Ass Lineage You can read more on the article below 🔽🔽🔽🔽 https://lnkd.in/eMRYzdcv

Today I'm at Snowflake Summit 2024 and excited to announce that Bigeye now automatically maps end-to-end lineage for analytics dashboards across cloud data warehouses, ETL tools, and legacy data sources! This is an industry first- data observability companies have offered basic data lineage for many years, but no one has been able to crack how to trace column level lineage from end-to-end, especially in enterprise environments. Want to learn more? 👉 https://hubs.la/Q02zw77p0

If one of your 2024 goals is to eliminate time spent on data pipeline maintenance- allow us to help you 🙌 In our latest case study, we walk you through how Fundrise used dbt Cloud to achieve: ✅ >50% reduction in time to introduce new data sources ✅ 0 time spent on data pipeline maintenance Get all the details, below ⬇️