Announcing Data Observability for Firebolt
Overview
Firebolt is the cloud data warehouse designed for next-gen analytics experiences. The platform's ease of use of modern architecture with a sub-second performance at a terabyte-scale has enabled many companies to adopt it in the recent past.
So when a months back, a channel partner company said: You got to build an integration with Firebolt! I almost felt like Firebolt was the coolest kid on the block with whom we had to make friends :-)
After reading their white papers, the data geek in me felt even more strongly that we had to make friends with this cool kid, and Max couldn't agree more.
So here we go. As a complete data pipeline Observability tool, Telmai now supports Firebolt along with many other DataWarehouse and datalake integrations like Snowflake, GCS, S3, AzureBlog, and BigQuery.
Telmai + Firebolt
As a low-code, no-code data observability product Telmai helps data teams proactively detect and investigate data issues within Firebolt before having a downstream impact.
Data teams struggle to detect data quality issues as soon as they occur — these issues are often first experienced by consumers of ML and Analytics and cause long and tedious investigation cycles. Telmai helps reduce the mean time to detect (MTTD) and mean time to resolve(MTTR) data quality issues.
Benefits for Firebolt users :
- Automatic monitoring on 40+ predefined data metrics like schema change, row count, completeness, uniqueness, patterns, distribution change, freshness, accuracy, etc
- Simple UI for expectations/rules - no more hand-coded rules using Great Expectation or DBT expectations.
- Support for structured & semi-structured data, i.e., nested and multi-valued attributes.
- Change data capture so users can independently monitor the entire dataset and changes alone.
- Support multiple formats like JSON, Parquet, CSV, and Avro
- Intuitive human-in-loop model for fine-tuning thresholds, policies, or writing expectations
- Spark processing to enable infinite scale without performance impact on Firebolt queries.
How does this work
How to get started ?
- Create a free Telmai account https://www.telm.ai/get-started
- Within minutes you will get your account with onboarding instructions
- Connect to Firebolt data source
- You are all set. You automatically get monitored.
Summary
Firebolt users can now continuously monitor their data in Firebolt for Key Data quality metrics without impacting performance. This no-code integration ensures that the data inside Firebolt is always trustworthy for Analytics.
Feel free to get your trial account or schedule a demo @ www.telm.ai
Data profiling helps organizations understand their data, identify issues and discrepancies, and improve data quality. It is an essential part of any data-related project and without it data quality could impact critical business decisions, customer trust, sales and financial opportunities.
To get started, there are four main steps in building a complete and ongoing data profiling process:
We'll explore each of these steps in detail and discuss how they contribute to the overall goal of ensuring accurate and reliable data. Before we get started, let's remind ourself of what is data profiling.
1. Data Collection
Start with data collection. Gather data from various sources and extract it into a single location for analysis. If you have multiple sources, choose a centralized data profiling tool (see our recommendation in the conclusion) that can easily connect and analyze all your data without having you do any prep work.
2. Discovery & Analysis
Now that you have collected your data for analysis, it's time to investigate it. Depending on your use case, you may need structure discovery, content discovery, relationship discovery, or all three. If data content or structure discovery is important for your use case, make sure that you collect and profile your data in its entirety and do not use samples as it will skew your results.
Use visualizations to make your discovery and analysis more understandable. It is much easier to see outliers and anomalies in your data using graphs than in a table format.
3. Documenting the Findings
Create a report or documentation outlining the results of the data profiling process, including any issues or discrepancies found.
Use this step to establish data quality rules that you may not have been aware of. For example, a United States ZIP code of 94061 could have accidentally been typed in as 94 061 with a space in the middle. Documenting this issue could help you establish new rules for the next time you profile the data.
4. Data Quality Monitoring
Now that you know what you have, the next step is to make sure you correct these issues. This may be something that you can correct or something that you need to flag for upstream data owners to fix.
After your data profiling is done and the system goes live, your data quality assurance work is not done – in fact, it's just getting started.
Data constantly changes. If unchecked, data quality defects will continue to occur, both as a result of system and user behavior changes.
Build a platform that can measure and monitor data quality on an ongoing basis.
Take Advantage of Data Observability Tools
Automated tools can help you save time and resources and ensure accuracy in the process.
Unfortunately, traditional data profiling tools offered by legacy ETL and database vendors are complex and require data engineering and technical skills. They also only handle data that is structured and ready for analysis. Semi-structured data sets, nested data formats, blob storage types, or streaming data do not have a place in those solutions.
Today organizations that deal with complex data types or large amounts of data are looking for a newer, more scalable solution.
That’s where a data observability tool like Telmai comes in. Telmai is built to handle the complexity that data profiling projects are faced with today. Some advantages include centralized profiling for all data types, a low-code no-code interface, ML insights, easy integration, and scale and performance.
Data Observability
Data Quality
Leverages ML and statistical analysis to learn from the data and identify potential issues, and can also validate data against predefined rules
Uses predefined metrics from a known set of policies to understand the health of the data
Detects, investigates the root cause of issues, and helps remediate
Detects and helps remediate.
Examples: continuous monitoring, alerting on anomalies or drifts, and operationalizing the findings into data flows
Examples: data validation, data cleansing, data standardization
Low-code / no-code to accelerate time to value and lower cost
Ongoing maintenance, tweaking, and testing data quality rules adds to its costs
Enables both business and technical teams to participate in data quality and monitoring initiatives
Designed mainly for technical teams who can implement ETL workflows or open source data validation software
Start your data observibility today
Connect your data and start generating a baseline in less than 10 minutes.
No sales call needed
Start your data observability today
Connect your data and start generating a baseline in less than 10 minutes.
