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Niceshops Cuts Data Ingestion Costs 50%

DoiT helped the 40-store ecommerce group unify siloed data on Google Cloud with BigQuery and Looker pipelines built for self-service analytics.

Meet Niceshops

Niceshops is an e-commerce retailer that operates around 40 different shop portals. Each store focuses on a specific product niche, from electric bikes to 3D printing or cosmetic products. A team of more than 500 people brings these shops to life. Every employee brings different core skills to the table, and the company wants to leverage those skills by enabling employees to make their own decisions with the right data at their fingertips.

The Challenge

Niceshops had isolated data sources across 40+ shops including sales histories, financial reports, and performance marketing data. They needed to unify these sources in a single warehouse to create holistic overviews and enable self-service data models for employees to make better decisions.

The Solution

DoiT provided managed services and support to help Niceshops expand their Google Cloud data platform using BigQuery and Looker. The team built advanced data engineering pipelines, implemented cost monitoring frameworks, and received training on best practices for data pipeline development and BigQuery ML model optimization.

Results

Achieved 50% savings on data ingestion expenses through optimized monitoring and queries
Reduced cloud spend by 30% by fixing BigQuery ML model configurations in Looker
Transformed marketing calculations from week-long manual processes to 30-minute automated insights
Eliminated data silos and became a truly data-driven company with self-service analytics

With DoiT's help, we were able to set up a monitoring framework that gives us a daily overview of our costs. This helped us detect and avoid cost peaks and improve our queries. We're estimating that we can lower our data ingestion expenses by 50%, thanks to support from DoiT.
Stefan Gajanovic, Data Engineer, Niceshops

Building self-service data models with Google Cloud

To empower employees with data tools, Niceshops had to overcome data silo problems across 40+ shops with isolated data sources like sales histories, financial reports, and performance marketing data. They needed to unify these sources in a single warehouse. The team selected Google Cloud due to their strong collaboration track record and Google's leadership in cloud-based data warehousing. They combined BigQuery's data warehouse capabilities with Looker's analytics features to create self-service models and cross-department analytical insights.

Expanding the data platform with DoiT

Employees loved the new self-service data opportunities and wanted more data sources and usage options. To better leverage the Google Cloud tool stack and expand the platform, Niceshops enlisted DoiT's support. DoiT became their trusted guide through Google Cloud technologies, helping navigate the landscape to create the best tech stack for their needs. With managed services and enterprise-level support, DoiT helped the team make the most of their Google Cloud setup.

Tapping into DoiT's knowledge to build new data pipelines

With DoiT's support, Niceshops expanded their data platform with more advanced data engineering and analytics pipelines. New data sources included competitors' price monitoring, marketing insights, and financial reports. The team built new pipelines in-house to replace legacy third-party ingestion tools. DoiT provided training sessions on building data pipelines, sharing useful tips and best practices for orchestration, scheduling, and monitoring that helped develop new pipelines quickly.

Speeding up development with first-class support

When the Niceshops team encountered issues they couldn't resolve in-house, DoiT was the first point of contact. Support tickets submitted via the DoiT Console were usually resolved the same day, speeding up development. DoiT often had answers ready because they had experienced similar issues with other customers. This support kept Niceshops on the right track, ensuring they weren't using tools incorrectly or choosing non-scalable solutions.

Making data-based decisions the new normal

Data silos are now eliminated at Niceshops. The online retailer became a truly data-driven company with holistic business metrics overview enabled by the new data platform. With just a few clicks, employees can get detailed insights into operations and market aspects, leveraging these insights for better business decisions. Marketing particularly benefits, with the platform helping identify shopping patterns of customer segments, informing ad campaigns and improving customer communication while reducing manual work from weeks to hours.

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What they say

What I really like about DoiT's approach is that you're very hands-on and proactive. Satyam would ping me a few times a sprint, letting me know about the most current features, checking in on how things are going. When we are going through a peak time, that proactiveness makes a real difference. Satyam always comes through whenever we need support and helps us leverage the right experts to get us where we need to be.

Chiamaka Ibeme, Engineering Manager, Platform

SELECT has made important cost data readily accessible. I will often pull it up during engineering design reviews so we can quickly evaluate cost impact and projections and factor that into our design decisions.

Douglas Zickuhr, Senior Data Platform Engineer at Personio

I love clicking through SELECT to understand how our environment and workloads are evolving. I probably check it every day. It's coffee and SELECT for me every morning.

Ian Fahey, Senior Analytics Engineer at Loop

You guys have the best UI experience that I've had of any software. It's like you just read my mind where, like, oh, I wish I could click there. Oh, I can

Diana Koshy, Sr. Director of Data Engineering at Kargo

SELECT feels like exactly what Paul and I would have built if we had locked ourselves in a room for 18 months to create our ideal monitoring solution.

Devin McGee, Data Engineering Lead at Home Chef

SELECT dramatically lowers the cognitive load to understanding Snowflake costs. I'm able to sit there and easily understand what's driving the cost. Not to blow smoke up your ass, but it's just so easy to do in your platform

Blake Baggett, Head of Data Operations at Entain

One of the most helpful cost rituals we've setup from SELECT is the weekly spend digest sent to Slack. I can start high level and ensure things are in check. If not, I can very quickly drill down into specific workloads which may have driven the cost spike and remediate them before they become a bigger issue.

Michael Revelo, Manager of data and analytics engineering at ClickUp

Through SELECT's automated savings feature and deep cost visibility, we were able to instantly lower our Snowflake spend by over 40% and achieve a 20X ROI on our SELECT investment.

Skyler Chi, SVP, GTM Productivity & Excellence at Exiger

Our costs had jumped up 3X as we scaled, so we're talking about 60% savings in Snowflake spend after adopting SELECT.

Edward Mancey, GTM Lead at Synthesia

DoiT was a true partner, not a vendor. They helped us understand the problem, refine the vision, and build something production-ready far faster than we could have on our own. Their expertise, responsiveness, and commitment made all the difference.

Dr. Anish Kapur, Founder & CEO, Promptly

DoiT's Customer Success and Forward Deployed Engineering teams work very closely with us. The regular sessions with our CSM keep us focused on the right priorities, and the FDEs provide the deep technical guidance we need to validate decisions and optimize our environment. That combination has been genuinely valuable for us.

Alexander Lundberg Santos, Platform Engineer at Extenda Retail

Every customer has unique usage patterns. Manual resource optimization simply didn't scale—we needed automation to ensure every customer, regardless of size, had right-sized infrastructure without consuming our team's capacity.

Brad Quinn, Lead Platform Engineer, PlayHQ

SNCF

PerfectScale allowed us to grow capacity without growing cost. We effectively absorbed 30% more usage for free.

Thomas Comtet, Senior Staff Engineer, SNCF

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