Lightdash is built for data engineers who are serving the rest of the company. There was no tool specialized around this persona - who wants the power to work like a software engineer & then enable business users to adopt analytics.
Collectors’ first considered Looker, Tableau, Mode, Sigma, and Preset. They later discovered Lightdash and chose it since it offered them
Pricing per user becomes a limiting factor for the adoption of BI tools within an organization. For starters, the buyer has to estimate spends based on the number of licenses & then negotiate who gets viewer or editor access. Lightdash changes this with a flat monthly pricing - removing hurdles for everyone in the organization to access insights.
While there’s been a lot of progress on semantic layers where business users can ask questions and get answers, we haven’t reached a place where there are no intermediaries involved. Data engineers still have to be involved in setting up the semantic layer that showcases an accurate answer.
AI is primarily being used to generate code that data engineers can use. It speeds up the process & increases developer productivity. But it’s not at a stage where there’s substantial impact.
“So when we launched on Hacker News, we gave ourselves a really verbose title and then somebody in one of the first comments put, oh, it's like open source looker. And I thought that was a much more concise way of describing what we were doing. So we actually adopted that tagline for a while.”
Hamzah Chaudhary
“The most successful way to get self-service analytics to work is by having folks - analytics engineers, dedicated to enabling the rest of the organization. These engineers could build semantic layers within data engineering tools & then expose it to an end-user.”
Hamzah Chaudhary
“My prediction is that the dbt metric flow product is going to be amazing and the pricing considerations around it will raise a lot of scrutiny, outrage and give other players in the space a chance to gain some adoption. So unfortunately, that's my prediction there from what I've seen with dbt's pricing at this time.”
Rohan Thakur
Discover how data management is evolving from the Modern Data Stack (MDS) to Intelligent Data Platforms. Our guest, Sanjeev Mohan, sheds light on the reasons behind this shift and its impact on businesses. Tune in to learn about the challenges with MDS, the rise of Intelligent Data Platforms, and what it means for organizations.
In this episode, Sadie, Chief AI Officer at SSL Innovations, talks about how AI has evolved. She covers the move from discriminative models to embracing Gen AI models, the need for a robust data strategy before adopting AI projects, and opportunities for career growth in the AI field. Join us for valuable insights into the changing AI landscape and its strategic aspects.
Join Jessica Talisman, as she sheds light on how Amazon leverages data, how information architecture shapes the company's data landscape, and the pivotal role humans play in AI applications.
Ep 03
Ep 02
Ep 01
Reverse ETL was once an obscure term, now stands as a crucial element in the Modern Data Stack. What sparked this transformation? Join Tarush Aggarwal and Boris Jabes, CEO of Census, as they unravel the evolution of Reverse ETL, share predictions for the data space, and shed light on Census's role in driving business growth.
Join host Tarush Aggarwal with Lightdash Founder Hamzah Chaudhary and Collectors' Director of Analytics, Rohan Thakur. They discuss Lightdash's unique position in BI, catering to data engineers for seamless analytics adoption. The episode delves into Lightdash's disruptive pricing model and explores the shift towards Conversational BI. Insights on AI in data engineering teams provide a snapshot of current advancements. Tune in for a straightforward conversation on Lightdash's impact, practical advantages, and its role in shaping the future of Business Intelligence.
