Data Engineering Consulting & Implementation Services
Expert data engineering consulting from a team that builds and scales production data platforms. BigData Boutique helps engineering teams design, implement, and optimize data pipelines, streaming architectures, and analytics infrastructure — from Kafka and Flink to Iceberg and ClickHouse.
We tried several different support services, which weren't very useful to us. With BigData Boutique we had the best experience.
You can tell just by talking to BigData Boutique, how deep their knowledge in search technology is.
BigData Boutique preserved the basic schema and underlying architecture, yet took fundamental design decisions that significantly improved the existing platform.
BigData Boutique created the best solution possible, and automated the entire process using DevOps principles.
Our Data Engineering Services
We bring deep, hands-on experience across the modern data stack — from ingestion and transformation to storage, processing, and analytics — so your team can focus on delivering value instead of fighting infrastructure. Our engagements span pipeline engineering, platform modernization, and big data analytics consulting when the goal is answers from the data, not more plumbing.
Data Pipeline Architecture
Design and implementation of robust data pipelines — batch ETL, real-time streaming, or hybrid ELT patterns processing billions of events per day
Real-Time Streaming
Streaming architectures with Apache Kafka and Apache Flink — event-driven microservices, real-time analytics, and sub-second data processing
Data Lakehouse & Storage
Modern lakehouse architectures with Apache Iceberg, Delta Lake, and open table formats — consolidating warehouses, reducing costs, and enabling cross-engine interoperability
Analytics Infrastructure
Purpose-built analytics platforms with ClickHouse, Elasticsearch, and OpenSearch — powering dashboards, reporting, observability, and product-facing analytics at scale
Migration & Modernization
Migrate from fragile cron-based scripts and monolithic ETL jobs to modern, observable, and scalable pipeline architectures — without disrupting existing workloads
Cost & Performance Optimization
Audit existing pipelines and storage layers, identify inefficiencies, and implement changes that reduce cloud infrastructure costs and improve throughput
Where Teams Bring Us in for Data Engineering
The companies that hire our data engineering consultants rarely need a slide deck. They need a pipeline that stops breaking, a bill that stops growing, or a platform that is ready before the next product launch. These are the situations we see most often.
Pipelines that fail silently
Cron jobs and Airflow DAGs that break at 3 AM, backfills nobody trusts, and no way to tell which dashboard is stale. We add observability, idempotent reruns, and data quality checks, then restructure the jobs that cause most of the pages.
Batch is too slow for the product
A nightly load was fine until the product needed fraud scores, recommendations, or alerts in seconds. We design and build the streaming layer with Kafka and Flink, including exactly-once guarantees and backpressure that holds under real load.
The warehouse bill outgrew the data
Compute spend that doubles every year while query volume does not. We move cold and semi-structured data to an Iceberg lakehouse, put hot analytics on ClickHouse, and right-size the jobs that remain, typically cutting the bill by 30 to 60 percent.
Migrating off a legacy platform
Hadoop clusters, on-premises ETL tools, or a vendor contract that is up for renewal. We plan and execute the migration to a cloud-native platform in phases, with both systems running until every consumer has been moved and verified.
A team stretched too thin
Two engineers, fifteen pipelines, and a roadmap that assumes five. Our senior data engineers embed with your team for a quarter or two, ship the platform work, and leave you with documentation and runbooks instead of a dependency.
Data that is not ready for AI
RAG and agent projects stall because the data is scattered, stale, or impossible to join. We build the CDC, feature, and embedding pipelines that give your AI work a reliable foundation. See our AI consulting services for the layer on top.
Technologies We Work With
| Category | Technologies |
|---|---|
| Stream Processing | Apache Kafka, Apache Flink, Kafka Connect, Kafka Streams |
| Batch & ETL | Apache Spark, dbt, Apache Airflow |
| Lakehouse & Storage | Apache Iceberg, Delta Lake, Databricks, Apache Hudi |
| Analytics Engines | ClickHouse, Elasticsearch, OpenSearch |
| Cloud Platforms | AWS (Glue, EMR, Kinesis, Redshift), GCP (BigQuery, Dataflow), Azure (Synapse, Data Factory) |
| Data Integration | Airbyte, Fivetran, custom connectors |
We are technology-agnostic. We recommend and implement the tools that fit your requirements — not the ones that generate the most vendor revenue.
Why Choose BigData Boutique for Data Engineering?
Deep Specialization
Our team includes recognized experts in search, streaming, analytics, and distributed systems. We contribute to open-source projects and speak at industry conferences.
Production Mindset
We design for reliability, observability, and operational simplicity. Every architecture decision accounts for what happens at 3 AM when something breaks.
End-to-End Delivery
From initial architecture review through implementation and handoff, we stay involved until the system is running well and your team is confident maintaining it.
Proven Track Record
Our clients include Fortune 100 companies and fast-growing startups. We have delivered data engineering solutions across financial services, e-commerce, cybersecurity, and healthcare.
How We Work
Discovery & Assessment
We audit your current data infrastructure — pipelines, storage, processing patterns, and pain points — to identify opportunities and risks.
Architecture Design
We design an optimized data architecture tailored to your workloads — technology selection, pipeline topology, storage strategy, and cost modeling.
Implementation
Our engineers build hands-on alongside your team — pipelines, integrations, migrations, and infrastructure-as-code — with production-grade quality from day one.
Handoff & Support
Documentation, knowledge transfer, and optional ongoing support to keep your data platform healthy, performant, and cost-efficient.
Data Engineering Projects We Have Delivered
A few of the data platforms our consultants have designed, built, or rescued. More are on our customer stories page.
The First International Bank of Israel adopted a new data platform and brought in BigData Boutique for end-to-end pipeline construction, performance tuning, and bespoke ETL services for Kafka event streaming, all delivered under strict banking security and regulatory requirements.
EverC worked with BigData Boutique to regain control of its data platform costs on AWS by migrating Spark workloads to EMR on EKS, without disrupting analyst workflows or losing operational flexibility.
Digipal partnered with BigData Boutique to design and build an end-to-end IoT asset tracking and analytics platform on AWS from day one, going from vision to a revenue-enabling product at speed.
Ways to Work With Our Data Engineering Consultants
You meet the engineers who will do the work and see the plan before any hour is billed. Pick the model that matches the problem, and switch later if the problem changes.
Fixed-scope project
An assessment, a migration, or a new pipeline with a defined deliverable, timeline, and quote.
Hours bank / retainer
A block of senior engineering hours you draw on for architecture reviews, incidents, and ongoing optimization.
Embedded engineer
A part-time or full-time data engineer working inside your team for a quarter or longer, without a hiring cycle.
Production support
24/7 coverage with an SLA for the platforms we support, through our enterprise support plans.
Frequently Asked Questions
What data engineering consulting services does BigData Boutique offer?
We provide end-to-end data engineering consulting including pipeline architecture, real-time streaming with Kafka and Flink, data lakehouse design with Apache Iceberg, analytics infrastructure with ClickHouse and OpenSearch, cloud platform optimization, and legacy pipeline modernization.
Which technologies do you work with?
We work across the modern data stack: Apache Kafka, Apache Flink, Apache Spark, Apache Iceberg, Delta Lake, ClickHouse, Elasticsearch, OpenSearch, dbt, Airflow, Databricks, and all major cloud platforms (AWS, GCP, Azure). We are technology-agnostic and recommend the best fit for your requirements.
Can you help modernize our legacy data pipelines?
Yes — this is one of our most common engagements. We help teams migrate from fragile cron-based scripts, monolithic ETL jobs, and legacy Hadoop workloads to modern, observable, and scalable pipeline architectures without disrupting existing workloads.
How do you help reduce data infrastructure costs?
We audit your pipeline architecture, storage layers, compute usage, and cloud configuration. By right-sizing resources, implementing efficient data tiering, optimizing query patterns, and eliminating redundant processing, we consistently deliver significant cost reductions.
Do you build data platforms from scratch?
Absolutely. For startups and growth-stage companies building their first data platform, we provide architecture guidance and hands-on implementation. We help you avoid the common pitfalls that lead to costly rework down the line.
How is data engineering consulting priced?
It depends on the engagement model. Assessments, migrations, and pipeline builds with a clear deliverable are quoted as fixed-scope projects. Ongoing work runs on a pre-paid hours bank or monthly retainer, and longer placements use an embedded engineer at a monthly rate. The rate is set by scope, seniority, duration, and any SLA you need. We do not publish list prices, but you will have a written quote before any work starts.
Is there a minimum engagement size?
No long-term commitment is required. Many clients start with a one- to two-week architecture assessment or a small hours bank, then decide whether to continue once they have seen how we work.
How quickly can you start?
A scoping call usually happens within a few days of your first message, and most engagements start within one to two weeks of agreeing on the plan. Urgent production issues can be handled faster through our enterprise support plans.
Will we meet the engineers before we commit?
Yes. The engineer who will lead the work joins the scoping call, and you receive a written plan with scope, milestones, and the people assigned before any hour is billed.
Can your data engineers join our team for a few months?
Yes. Our embedded engineer model places a senior data engineer with your team part-time or full-time, typically for a quarter or more. They work in your repositories and processes and leave behind documentation and runbooks, not a dependency on us.
Do you work with Databricks, Snowflake, AWS, GCP, and Azure?
Yes. We are vendor-neutral and work in whichever cloud and platform you already run, including Databricks, Snowflake, BigQuery, Redshift, EMR, Glue, Dataflow, and Synapse. When a platform choice is still open we recommend based on your workloads and budget, not on partner incentives.
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