01
Pipelines and ELT
Managed connectors such as Fivetran or Airbyte, or custom Python where no connector exists, loading data from your apps, databases and SaaS tools.
FIG 01 · Data Engineering
Data engineering services that make your data ready for dashboards and AI
ELT pipelines from your apps and SaaS tools, dbt models with tests, and a Snowflake, BigQuery or Databricks warehouse your team can trust, feeding Power BI dashboards and AI features.
See how it works
FIG 02 · Quick answer
What are data engineering services?
Data engineering services design, build and run the pipelines that move data from your apps and SaaS tools into a warehouse, clean and model it, and keep it reliable. Elgrith builds ELT pipelines, dbt models with tests, and Snowflake, BigQuery or Databricks warehouses that feed Power BI dashboards and AI features.
- Tested source of truth for dashboards and AI
- 1
- Projects delivered since 2015
- 1,000+
- Clients across the USA, UK and India
- 250+
- Your warehouse, pipelines and code
- 100%
FIG 03 · Why it matters
AI projects stall on data. So do dashboards.
From a Redpoint survey for Fivetran (May 2025, 401 respondents) and dbt Labs’ State of Analytics Engineering 2025.
01
42%
Of enterprises report most AI projects held back
Fivetran’s survey: “42% of enterprises say more than half of their AI projects have been delayed, underperformed, or failed”. The report ties it to poor data readiness.
Source: Fivetran press release, 13 May 2025 (Redpoint survey, n=401)(opens in a new tab)02
56%
Name data quality as a problem
In dbt Labs’ 2025 survey, “56% of survey respondents identified data quality as a problem.” Tests and freshness checks are part of every model we ship.
Source: dbt Labs, State of Analytics Engineering 2025(opens in a new tab)03
67%
Spend most engineering effort on upkeep
The same Fivetran survey found “67% of centralized enterprises allocate over 80 percent of their engineering resources to maintaining data pipelines”. Managed connectors and tested models are how we keep upkeep down.
Source: Fivetran press release, 13 May 2025 (Redpoint survey, n=401)(opens in a new tab)
FIG 04 · Choose
Snowflake vs BigQuery vs Databricks: how we choose
All three are good. The right one depends on your cloud, your team’s skills and your workloads. We recommend one after an audit, and we hold no partner tier with any of them.
| Snowflake | Google BigQuery | Databricks | |
|---|---|---|---|
| What it is | A cloud data warehouse that runs on AWS, Azure or Google Cloud | Google Cloud’s serverless data warehouse | A lakehouse platform built on Apache Spark and Delta Lake |
| Strongest at | SQL analytics and sharing data across teams | Google Cloud stacks, GA4 and Google Ads data | Large-scale data engineering, machine learning and streaming |
| Skills it suits | SQL and dbt | SQL and dbt | Python, Spark and SQL |
| Watch for | Warehouse sizing and compute credits | Query costs on large scans | Cluster setup and platform complexity |
| Often our pick when | You want one warehouse that works across clouds | You already run on Google Cloud | Machine learning and big data lead |
Already on Microsoft? Microsoft Fabric is an option too, and Amazon Redshift suits AWS-only stacks.
Moving data out of an old system? Read our data migration checklistFIG 05 · What you get
What data engineering services include
From the first connector to data your dashboards and AI features can trust, built as code you own.
02
Data integration
Data integration engineering across CRM, ERP, ecommerce, finance and product data, with matching keys so customers and orders line up.
03
Warehouse and lakehouse setup
Snowflake, BigQuery, Databricks, Redshift or Fabric set up with environments, roles and cost controls, as infrastructure as code.
04
dbt modeling and testing
dbt models in version control, with tests for uniqueness, nulls, relationships and accepted values, plus docs and lineage generated from the code.
05
Real-time streaming
Kafka or change data capture (CDC) streams where minutes matter, such as stock levels, fraud signals or device data.
06
Data migration
Moves from legacy databases, spreadsheets or an old warehouse, cleaned, mapped and reconciled, with record counts checked on every run.
07
Data quality and observability
Freshness checks, volume and schema alerts, and lineage, so a broken source is caught before someone opens a dashboard.
08
AI-ready data
Clean, documented tables, plus embeddings in a vector store such as pgvector, ready for RAG and AI agents.
Deliverables
12
deliverables, in writing
Included in every build, written into your fixed quote before work starts.
FIG 06 · Example flow
From raw data to dashboards and AI
One pipeline, end to end. Every step is code in version control, tested and owned by you.
- App database
- CRM
- Online store
- Ads and analytics
Warehouse
- Dashboards
- Back to your CRM
- Embeddings for RAG
- Data science
1. Extract and load
- From
- Apps, databases and SaaS tools
- With
- Fivetran, Airbyte or custom Python
- Cadence
- Scheduled, or streamed with CDC
2. Land raw data
- To
- A raw schema in the warehouse
- Kept
- As it arrived
- Why
- Replays and audits without re-extracting
3. Model with dbt
- Layers
- Staging, intermediate and mart models
- Tests
- Uniqueness, nulls and relationships
- Docs
- Generated from the code
4. Test and monitor
- Checks
- Freshness, volume and schema changes
- Alerts
- Slack or email to the owner
- Why
- Bad data caught before dashboards
5. Orchestrate
- With
- Airflow, Dagster or dbt jobs
- Handles
- Dependencies and retries
- Why
- One run order everyone can see
6. Serve dashboards
- To
- Power BI, Looker Studio or Tableau
- Via
- A semantic layer with shared metrics
- Why
- One number for one question
7. Feed AI
- To
- Embeddings in pgvector or a vector database
- For
- RAG chatbots and AI agents
- Refresh
- With the pipeline
8. Govern
- Access
- Roles and masking for personal data
- Lineage
- From source to dashboard
- Why
- Know who sees what, and where it came from
FIG 07 · Flagship build
Proof: AI products we design and engineer
Kenerate AI is our flagship build, an all-in-one AI studio designed and engineered by Elgrith. It is not a data warehouse; it shows the AI product engineering that clean data feeds: many models and tools behind one app.
- 200+ AI models in one app, with one login and one credit balance
- Kenerate Agent chat, plus MCP & CLI inside Claude, ChatGPT, Cursor and 19+ AI apps
- 23+ studios and tools, with 500+ guides
- 1,000+ projects for 250+ clients since 2015
Tools: 23+ studios with guides for every use caseDesigned and engineered by Elgrith
- AI models in one app
- 200+
- Studios and tools
- 23+
- Maximum output
- 4K
- Guides
- 500+
FIG 08 · Quality
Data quality, observability and governance
56% of respondents to dbt Labs’ 2025 survey named data quality as a problem. We treat quality as a deliverable, not an afterthought.
06principles
Tests on every model
dbt tests for uniqueness, nulls, relationships and accepted values, run on every change and every load.
Freshness checks
Each source has a freshness target, and a stale source raises an alert before anyone reads an old number.
Lineage
From source table to dashboard tile, so you can see what breaks when something upstream changes.
Alerts with owners
Every alert goes to a named owner in Slack or email, with a runbook for the fix.
Access control and masking
Role-based access and masking for personal data, designed around UK GDPR and US privacy expectations.
Documentation
Model and metric definitions generated from the code, so the docs stay current.
FIG 09 · AI
From warehouse to AI: data ready for RAG and agents
AI features are only as good as the data behind them. We prepare that data as part of the pipeline.
06principles
Clean, documented sources
The documents and tables your AI will use, cleaned, de-duplicated and described.
Chunking and embeddings
Documents split and embedded in a repeatable pipeline, not a one-off notebook.
Vector store
pgvector in your Postgres, or a managed vector database when scale calls for it.
Permissions carried through
Users only retrieve what they are allowed to see in the source system.
Refreshed with the data
Embeddings update when the source changes, on the same schedule as the rest of the pipeline.
Into agents and chatbots
Clean, permissioned data for the AI agents and RAG chatbots we build.
AI agent development
FIG 10 · Choosing
How to choose a data engineering service provider
Questions to ask any data engineering company, us included.
06principles
Do they write tests?
Ask what happens when a source sends nulls or duplicates. Tests should catch it, not your finance team.
Will you own the code?
The dbt project, the pipeline code and the cloud accounts should be yours, in your Git and your cloud.
Do they recommend or resell?
Ask whether their platform advice depends on a partner tier. Ours doesn’t: we hold none.
Can they explain running costs?
Warehouse and connector bills grow with data. A good provider designs cost controls from the start.
Do they think past the warehouse?
Data is only useful in dashboards, products and AI. Ask how theirs gets used.
Can your team run it?
Docs, runbooks and training, so you aren’t locked into the people who built it.
FIG 11 · Process · 7 steps
Data engineering consulting: audit, roadmap, build
Start with an audit and a roadmap, or go straight to the build. Either way you get a written scope and a fixed quote first.
- Step 01
Audit
Your sources, current reports, pain points, costs and skills. We find what is broken, duplicated or not trusted.
- Step 02
Roadmap
A target architecture, a platform recommendation and a phased plan that starts with the data that matters most. You get a written scope and a fixed quote.
- Step 03
Foundation
Warehouse, environments, access roles and cost controls set up as code in your cloud account.
- Step 04
Pipelines
Sources connected and raw data landed, with monitoring from the first load.
- Step 05
Models and tests
dbt models for your core entities and metrics, with tests and docs, reviewed in weekly demos.
- Step 06
Serve
Dashboards, syncs back to your CRM or AI features built on the tested models.
- Step 07
Handover and run
Runbooks, training and a handover to your team, or ongoing support on terms agreed in your written scope.
FIG 12 · What you get
Everything included
Written into your fixed quote, so you know exactly what you are paying for before work starts.
12items
- Data audit and findings
- Target architecture and platform recommendation
- Written scope and fixed quote
- Warehouse set up as code
- ELT pipelines with monitoring
- dbt project with tests and docs
- Lineage from source to dashboard
- Freshness and quality alerts
- Cost controls and usage reports
- Data ready for dashboards and AI
- Runbooks and team training
- Code, credentials and docs, all yours
FIG 13 · Tech & tools
The tools behind the work
Proven, well-supported tools, chosen for your project and set up in accounts your business owns.
- Fivetran
- Airbyte
- dbt
- Apache Airflow
- Dagster
- Apache Kafka
- Snowflake
- Google BigQuery
- Databricks
- Amazon Redshift
- Microsoft Fabric
- Delta Lake
- Apache Iceberg
- PostgreSQL
- pgvector
- Python
- SQL
- Terraform
- Power BI
- Looker Studio
FIG 14 · Who it’s for
Data engineering for teams that have outgrown spreadsheets.
From a first warehouse to an AI-ready platform, in the US, the UK and India.
3 audiences
First warehouse
From exports to one source of truth.
Growing companies whose reports are spreadsheets stitched from exports, and who need their first warehouse done right.
- Core sources connected
- One set of metric definitions
- Dashboards your team trusts
- Costs kept in check
AI teams
Data ready before the model.
Product and AI teams building RAG, agents or machine learning features who need clean, permissioned data first.
- Documented, tested sources
- Embeddings refreshed with the data
- Permissions carried through
- Ready for LLM and agent builds
UK teams
A data engineering consultancy for UK teams.
UK clients work through our London client partner, with engineering in New Delhi. We design data handling around UK GDPR, with UK or EU hosting regions where you need them.
- London client partner
- UK GDPR-aware design
- UK or EU hosting regions
- Fixed written quote in GBP
FIG 15 · Working with us from the USA and UK
Built in New Delhi. Run on your hours.
Every project gets a point of contact in New York or London and an engineering team in New Delhi. Here is how today’s working days line up, live.
New York
North America
--:--ET
New Delhi team Your working day- Shared working time
- ≈ 3 hours today
- New Delhi is
- 10.5 h ahead
- Live calls, your time
- 9 a.m. to 12 p.m. ET
London
UK & Europe
--:--UK
New Delhi team Your working day- Shared working time
- ≈ 4.5 hours today
- New Delhi is
- 5.5 h ahead
- Live calls, your time
- 9 a.m. to 1:30 p.m. UK
Three ways to work together
- 01
Fixed-scope project
A written scope, one fixed price and a dated plan. Suits websites, apps and MVPs.
- 02
Dedicated team
Designers and engineers on your roadmap every month, billed monthly.
- 03
White label
For US and UK agencies: our work, your brand, unbranded deliverables.
Every engagement
- Fixed quote in USD, GBP or INR after a free 30-minute call
- NDA before discovery. You own all code, designs and content
- A working demo every sprint, not status slides
- Reply within one business day, calls inside your hours
Book a call
Calls on ET or UK hours
FIG 16 · Definitions
ETL vs ELT
Two ways to get data into a warehouse. For cloud warehouses, ELT is our default.
| ETL (extract, transform, load) | ELT (extract, load, transform) | |
|---|---|---|
| Where transformation happens | Before loading, in a separate tool or server | Inside the warehouse, after loading |
| Raw data kept | Often not | Yes, so you can replay and audit |
| Typical tools | Traditional ETL suites and custom scripts | Fivetran or Airbyte, then dbt |
| Best for | Data that must be masked before it lands, or legacy targets | Cloud warehouses and analytics |
| Our default | When rules require it | Most projects |
FIG 17 · Freshness
Batch or real-time pipelines?
Fresher data costs more to build and run. Pick per use case, not for the whole platform.
| Batch | Micro-batch | Real-time streaming | |
|---|---|---|---|
| Freshness | Hours, such as nightly | Minutes | Seconds |
| Typical tools | Scheduled ELT and dbt | Frequent syncs and incremental models | Kafka and change data capture |
| Cost and complexity | Lowest | Low to medium | Highest |
| Best for | Finance and management reporting | Operational dashboards | Fraud, stock and device data |
FIG 18 · Compare
Data engineering vs analytics engineering vs data science
| Data engineering | Analytics engineering | Data science | |
|---|---|---|---|
| Builds | Pipelines, warehouses and platforms | Tested models and metric definitions | Statistical models and experiments |
| Main tools | Fivetran, Airbyte, Kafka, Airflow, Python | dbt and SQL | Python, notebooks and ML libraries |
| Output | Reliable, fresh data in the warehouse | Clean tables for dashboards and analysis | Forecasts, predictions and insights |
| You need it when | Data lives in many systems | People argue about which number is right | You have clean data and a question to predict |
FAQ
Frequently asked questions
Straight answers about Data Engineering with Elgrith.
Data engineering services cover connecting your data sources, loading data into a warehouse or lakehouse, modeling and testing it with tools like dbt, orchestrating and monitoring the pipelines, and serving the results to dashboards, applications and AI features. Elgrith also handles data migration, real-time streaming and the access controls around personal data.
FIG 19 · Related
Keep exploring
Data Warehouse Consulting
Snowflake, Databricks & BigQuery buildsData Visualization
Dashboards, Power BI & TableauLLM Integration
ChatGPT, Claude & RAG in your productAI Development
LLM apps, AI agents & MCPCloud Migration
Move to AWS, Azure or Google CloudERP Development
Custom ERP & ERP integrationsCRM Development
Custom CRMs & HubSpot/Zoho setupsPython Development
Django, FastAPI & Python for AI
Free 30-minute call
Make your data worth trusting.
Tell us where your data lives today and what you want from it. After a free audit call you get a written scope, a platform recommendation and a fixed quote.
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