In The Data Engineering Show, Ryanne Dolan from LinkedIn joins the Bros to discuss LinkedIn's Hoptimator project.
In this blog, we’ll highlight how we fuzz Firebolt’s blazing-fast query processor written in modern C++
This blog describes our thinking and guiding principles behind design choices while building Firebolt.
Vin Vashishta, the guy we all love to follow, has never seen a dashboard with positive ROI.
This blog is a GA announcement of Firebolt’s next-gen cloud data warehouse that delivers low-latency analytics at scale.
Explore Firebolt's cost efficiency with real-world data benchmarks highlighting low latency and high concurrency.
Explore Firebolt's bulk ingestion benchmarks, highlighting speed, cost-efficiency, and performance.
Discover Firebolt's efficiency in incremental ingestion and DML workloads
Discover how Firebolt's primary index optimizes data handling for large-scale analytics, enhancing query performance.
Deliver efficient ELT with the combination of Firebolt's elastic infrastructure and the simplicity of SQL models on dbt.
This guide will provide you with the fundamental knowledge necessary to handle semi-structured data effectively.
Firebolt Inc., a cloud data warehouse provider, announced its next-generation compute infrastructure, Engines.
Scale one node at a time to adjust compute resources incrementally, ensuring an ideal price-performance ratio.
Read on to find out how Lurkit is using Firebolt over AWS to serve advanced gaming analytics.
Learn how the data management lifecycle looks like in Firebolt
Uncover Firebolt’s Engine internals featuring zero-downtime upgrades, multi-dimensional elasticity, and granular scaling
Technical deep dive on how Firebolt evolved into a PostgreSQL-compliant database system.
Deep dive into how Firebolt optimizes query performance through caching and reusing results of parts of the query plan.
Amplitude's cutting-edge data stack and how it processes 5 Trillion real-time events while dealing with mutable data
How does a tech stack that always needs to be at the forefront of technology look like?
Scaling a data platform to support 1.5T events per day requires complicated technical migrations
How does Substack's data platform support 500K paying subscribers?
Appsflyer deals not only with 120 billion events per day, but does so while growing quickly as a company
How Vimeo handles Data Ops to deal with massive scale?
Steven Moy thoroughly explains Yelp’s data architecture under the hood and how it evolved over the past ten years.
Canva is one of the hottest, if not the hottest, graphic design platforms out there.
Gong manages hundreds of thousands of videoconferences and millions of emails PER DAY, which add up to hundreds of TBs.
It’s the mother of all development projects. You use it daily. And so do 65M developers around the world.
Ananth Packkildurai is Principal Software Engineer at Zendesk and runs one of the strongest newsletters in data
Bolt engineers are in the midst of designing a new next-gen data platform
Why would you create ugly data? According to Jens Larsson, don’t even go near raw data.
Should data engineering AND BI be handled by the same people?
Klarna is one of the leading fintech companies in the world, valued at $45B.
An episode about Eventbrite’s data stack modernization process, and how you get engineers to adopt new technologies
How the data platform evolved as Slack grew from a startup to an IPOed and then acquired company.
Sudeep Kumar, Principal Engineer at Salesforce considers the shift to Clickhouse as one of his biggest accomplishments
According to Yoav Shmaria, VP R&D Platform at Similarweb, the best way to manage data warehouse costs is tagging
Max walks the Bros through his recipe for a smart data-driven company, and the genesis of Airflow, Superset & Presto.
80% of the code that you write doesn’t work on the first try. But knowing which 80% is not working is the real challenge
Barr Moses explains how to make sure your data is accurate in a world where so many different teams are accessing it
How ZipRecruiter and Yotpo build resilient self-service products that keep customers happy and engineers calm
How good you are at Spark or Flink ≠ how good you are at data engineering. Zach Wilson explains.
As people in the data industry go, Bill Inmon is among the top, often seen as the godfather of the data warehouse.
When it comes to data management, have we come a long way since the early 2000s?
Meenal Iyer, VP Data at Momentive.ai, talks about enforcing collaboration in large organizations
Joe Reis and Matt Housley joined the bros for some much-needed ranting, priceless data advice, and good laughs.
Every data team should have at least one data engineer with a software engineering background.
Megan Lieu about her approach to data advocacy as well as the power of notebooks
Joe Hellerstein and Joseph Gonzalez inspired generations of database enthusiasts and are now on the show
Data engineering should be less about the stack and more about best practices.
Principles essential for data quality, cost optimization, and data modeling, as adopted by the world's leading companies
Too often expensive resources and manhours are spent on dashboards no one uses, resulting in zero ROI.
Andy Pavlo, Associate Professor at Carnegie Mellon University, delves into database internals and optimization.
There’s so many data warehouses out there, who the hell needs another one? Three main things that make Firebolt unique.
IQVIA deep dive into maximizing impact of BI solutions for faster and more informed decision-making in healthcare.
In this blog, we focus on distributed query execution as an integral part of Firebolt.
Lear the top 10 tips of how to improve your cloud data warehouse performance.
Learn how to upgrade from Tableau extracts to Tableau live connection to deliver sub-seconds performance every time.
Upstart cloud data warehouse sees rapid growth in 2021, plans to double its workforce
How to accelerate Looker performance on Redshift, Snowflake and BigQuery? Short-term fixes and the long-term solutions.
Many programming languages are imperative – tell the compiler how to operate by providing the instructions in order.
The data warehousing market has gone absolutely mad over performance. Why is this the case?
Everything you needed to know about cloud data warehouses but were afraid to ask...
Amazon Athena engine version 2 - what’s new and big enough to call this a 2.0 release?
Explore the significant differences between ELT and ETL data integration processes and find the best option for you.
A detailed comparison of Snowflake vs. Redshift, by architecture, scalability, performance, use cases and cost.
Why even simple queries can be slow in cloud data warehouses and how Firebolt uses indexing to prune data and stay fast?
Working with semi-structured data can be more like a Jason (horror movie) Sequel than JSON SQL.
The funding included participation from Zeev Ventures, TLV Partners, Bessemer Venture Partners and Angular Ventures.
"In the beginning, there was a data mess". Don’t Panic, just read our data hitchhiker’s guide to cloud analytics.
Indexes are the primary way for users to accelerate query performance in Firebolt. Learn about them here.
How to choose the best analytics engine for each type of analytics.
Demand from engineering teams has skyrocketed since Firebolt emerged from stealth last year
Learn when to use Postgres, MySQL, in-memory databases, HTAP, or data warehouses to meet the 1 sec SLA in analytics.
How companies should avoid creating a slow many headed federated Gorgon out of out of Athena.
Learn some simple rules of thumb you can use to choose the best federated query engine for your company's needs.
More and more, people are asking me “how do you compare Snowflake and Databricks?” We did our best to answer.
Let us guide you through the process of identifying the performance bottlenecks in your query in just 5 simple steps.
We often get asked “what’s the difference between Firebolt and Snowflake?” and it reminds me of Frozen.
When do you need to shift from Redshift, and what are the alternatives? Learn here.
Making sense of a data lakes, delta lake, lakehouse, data warehouse and more.
If you’re using Amazon Athena, you may have seen these errors. About AWS Athena errors and how to deal with them.
Choosing the right data warehouse and analytics infrastructure on Amazon Web Services (AWS) can be confusing.
The technological concepts that make Snowflake so unique, and why it has proven to be so disruptful for the data space.
A checklist of criteria to help you determine which factors are most important for the success of your organization
How to Set Up Your Data Analytics Stack with Kafka, Hevo, and Firebolt.
There has been a lot of talk recently about Data Apps. That's what Firebolt is thinking about data apps.
How to enable sub-second analysis across billions of rows of customer behavior data: Part I - Setting up the load
One of the ways Firebolt is able to support data-driven applications is by leveraging aggregating indexes on the tables.
Are you spending more than you planned on your Data Warehouse? Analyze more. Use less compute resources.
Firebolt provides an alternative to Druid, delivering fast response times, high concurrency and the convenience of a Saa
In this post, we look at factors to consider when building a data warehouse.
Is Postgres truly the right engine for analytics?
How to ingest, store and query JSON data, for example, is a consistent question on the minds of customers.
How to support ad hoc analysis - Part 2: The right ad hoc analytics architecture
In our recent ‘Big Data Analytics for Gaming Workshop’ we let the audience do the talking, here’s a summary of the talk.
"When I see David Jayatillake and Tristan Handy comment on Firebolt's approach it is clear that Firebolt is on track."
Data Mesh is hot stuff. But from a technology perspective it’s still not very well defined.
How to support ad hoc analysis: Part 1 - The 4 requirements for an ad hoc analytics architecture
Event streams have always been problematic to analyze in SQL. This is how we do it.