ETL Pipeline & Integration That Automate Your Data Flow
You need an ETL pipeline that moves your data reliably, transforms it correctly, and never fails silently. Whether you want to hire an ETL pipeline company to connect your CRM, ERP, and marketing tools into a single analytics layer, bring in experienced data integration engineers to automate data integration between platforms that currently require manual exports, or need full data integration services covering extraction, transformation, real-time sync, and API development, the goal is always the same: get the right data to the right place at the right time without anyone copying a spreadsheet.
ETL pipeline development typically costs between $5,000 and $60,000 depending on the number of sources, transformation complexity, and whether you need batch or real-time processing. A simple 2 to 3 source batch pipeline costs $5,000 to $15,000. Complex multi-source pipelines with real-time streaming run $20,000 to $60,000.
Core Capabilities and Features
Warehouse Loading Pipelines
The most common type of data integration. Your data is extracted from operational systems and loaded into a cloud data warehouse (BigQuery, Snowflake, or PostgreSQL) where it is available for dashboards, reports, and ad-hoc analysis. These pipelines typically run on a schedule (hourly, daily, or on a specific trigger) and form the backbone of your analytics infrastructure.
- Extracts data from CRMs, ERPs, databases, marketing platforms, billing systems, and any system with an API
- Pipelines run on a schedule (hourly, daily, or on a specific trigger) and form the backbone of your analytics
- Data loaded into BigQuery, Snowflake, or PostgreSQL for dashboards, reports, and ad-hoc analysis
Real-Time and Event-Driven Pipelines
Some data cannot wait for a batch load. E-commerce transactions, application events, IoT sensor readings, and financial data often need to flow continuously. Your streaming pipelines are built using Apache Kafka, AWS Kinesis, or Google Pub/Sub that process events as they happen and deliver them to warehouses, dashboards, or downstream applications within seconds.
- Built using Apache Kafka, AWS Kinesis, or Google Pub/Sub to process events as they happen
- Delivers data to warehouses, dashboards, or downstream applications within seconds
- Real-time adds complexity and cost (typically 2 to 3 times more than batch) recommended only when genuinely needed
Reverse ETL and Custom Integrations
Reverse ETL takes enriched, modelled data from your warehouse and pushes it back to operational tools. Your sales team sees customer lifetime value directly in Salesforce. Your marketing team gets audience segments pushed into Meta Ads. Your support team sees product usage data in Zendesk. When off-the-shelf connectors do not exist, custom API integrations are built with REST and GraphQL clients including authentication, pagination, rate limiting, error handling, and retry logic.
- Reverse ETL built using Census, Hightouch, or custom scripts depending on your stack and volume
- Custom REST and GraphQL API clients with authentication, pagination, rate limiting, and error handling
- Every custom integration is monitored, documented, and designed to handle the reality that APIs break and change
Why It Matters
Every dashboard, every report, every AI model, and every automated workflow in your organisation is only as reliable as the pipeline that feeds it. If the pipeline is wrong, everything downstream is wrong. ETL pipelines are invisible infrastructure. When they work, nobody notices. When they fail, everything breaks. Your dashboards show wrong numbers. Your automated emails send stale data. Your machine learning model trains on incomplete information. And the worst part is that pipeline failures are often silent: the data looks fine, it is just not current. The cost of bad pipelines is not measured in engineering hours. It is measured in bad decisions made with confidence. A marketing team that doubles ad spend on a channel because the attribution data was incomplete. A finance team that reports the wrong revenue because a billing sync failed. An operations team that understocks because the inventory pipeline lagged by two days. The teams that get the most from their pipelines are the ones who treat data infrastructure with the same rigour as application infrastructure. They monitor it. They test it. They document it. And they invest in ongoing maintenance because they know that a pipeline without attention is a pipeline waiting to break. That is the standard every build is held to.
By the Numbers
29%
The average organisation runs 897 applications but only 29% are connected. Every disconnected system is an island of data that requires manual effort to use. Pipelines exist to bridge these gaps.
Source: MuleSoft Connectivity Benchmark, 2025
84%
Integration is hard. Most failures come from unclear requirements, poor error handling, and scope overload. Starting small, testing thoroughly, and monitoring continuously is how you beat that statistic.
Source: Integrate.io / Data Transformation Statistics, 2026
295%
Organisations that invest in proper data integration report a 295% return over three years, with top performers reaching 354%. The return comes from eliminated manual work, faster decisions, and fewer errors
Source: SQ Magazine / Data Analytics Statistics, 2026
10.3x
Companies with strong data integration achieve 10.3 times the ROI from AI initiatives compared to 3.7 times for those with poor connectivity. Pipelines are the foundation that makes AI investments pay off.
Source: MuleSoft Connectivity Benchmark, 2025
64%
Nearly two-thirds of organisations say data quality is their biggest problem. Quality starts in the pipeline: if extraction is incomplete, transformation is buggy, or loading has duplicates, every downstream analysis is compromised.
Source: Precisely / Data Integrity Trends Report, 2025
"A pipeline is not a script that runs once. It is infrastructure that runs every day, handles failures gracefully, and alerts you before bad data reaches a decision-maker. Build it like production software, because that is what it is."
Technologies
Our Tech Stack
Our Process
How we turn ideas into reality.
Extraction
Data is pulled from your source systems. CRMs (Salesforce, HubSpot), ERPs (NetSuite, SAP), databases (PostgreSQL, MySQL, MongoDB), marketing platforms (Google Ads, Meta, LinkedIn), billing systems (Stripe, QuickBooks), application APIs, spreadsheets, and flat files. Pre-built connectors (Fivetran, Airbyte) are used when they exist and custom API connectors are built when they do not.
Transformation
Raw extracted data is converted into a format that is consistent, clean, and useful for analysis. This includes data type standardisation, deduplication, null handling, business rule application (like calculating lifetime value from transaction history), joining data across sources, and aggregation. dbt (data build tool) is used because every transformation is SQL-based, version-controlled, tested, and documented.
Loading & Orchestration
Transformed data is moved into its destination: a data warehouse (BigQuery, Snowflake, PostgreSQL), a data lake, another application, or a BI tool. Loading is configured for efficiency with partitioning, clustering, and upsert logic. Apache Airflow or Dagster manages dependencies between pipeline steps if Step A fails, Step B does not run.
Monitoring & Alerting
Every pipeline includes health checks. Data freshness indicators track when each source was last loaded. Row count comparisons detect unexpected changes. Schema change detection catches when a source system adds or removes fields. Alerts fire via Slack, email, or PagerDuty when something breaks. Your dashboards never silently show stale data.
Pricing
Investment Overview
Number of Data Sources
Each source requires its own extraction logic, authentication, error handling, and testing. 3 sources is a simple project. 12 sources with different APIs is significantly more complex.
Batch vs Real-Time
Daily batch pipelines are simpler and cheaper to build and maintain. Real-time streaming with Kafka or Kinesis adds infrastructure, complexity, and monitoring. Expect 2 to 3 times the cost of batch.
Transformation Complexity
Passing data through unchanged is simple. Calculating derived metrics, joining across sources, deduplicating, and applying complex business rules takes more engineering time.
Everything we do at Techneth is built around making data move reliably between the systems that matter. If you want to understand our approach before committing, you can read more about our team and how we work. Or explore the full range of digital product and development services we offer, like etl pipelines and data integration. And if you already know what you need, get in touch directly and we will find time to talk.
Frequently Asked Questions
Everything you need to know about this service.
- What is the difference between ETL and ELT?
- ETL transforms data before loading it into the destination. ELT loads raw data first and transforms it inside the destination (usually a cloud warehouse). ETL is better when data needs to be cleaned or redacted before entering the warehouse (for privacy or compliance). ELT is faster, more flexible, and the modern standard for cloud warehouses that have cheap storage and powerful compute. Most of our projects use ELT with dbt for transformation.
- How long does it take to build an ETL pipeline?
- A simple batch pipeline connecting 2 to 3 sources to a warehouse takes 2 to 4 weeks. A complex multi-source pipeline with custom connectors, real-time streaming, and transformation logic takes 6 to 12 weeks. The biggest variable is the condition of your source data: clean, well-documented APIs are fast to connect. Messy data, undocumented systems, and proprietary formats take longer.
- What is Fivetran and do I need it?
- Fivetran is a managed data integration platform with 300+ pre-built connectors. It extracts data from your sources and loads it into your warehouse automatically. You need it (or a similar tool like Airbyte) if your sources are standard platforms (Salesforce, HubSpot, Google Ads, Stripe, PostgreSQL). You do not need it if your sources are all proprietary or if you have very few, simple integrations. Fivetran pricing scales with data volume, so it is cost-effective for moderate volumes but can get expensive at scale.
- What is dbt and why does it matter?
- dbt (data build tool) is the industry standard for data transformation. It lets you write transformations in SQL, test them automatically, track changes with Git, and generate documentation from the code. Before dbt, transformation logic lived in stored procedures, custom scripts, or proprietary tools with no testing and no version control. dbt brings software engineering practices to data: every change is code, every code change is tracked, and every output is tested.
- What is reverse ETL?
- Reverse ETL takes enriched, modelled data from your warehouse and pushes it back to operational tools. Your warehouse calculates customer lifetime value, then reverse ETL sends that value to Salesforce so your sales team sees it. Your warehouse builds an audience segment, then reverse ETL pushes it to Meta Ads so your marketing team targets it. We build reverse ETL using Census, Hightouch, or custom scripts depending on your stack.
- How do you ensure data quality in pipelines?
- Quality checks are built into every pipeline stage. Extraction checks verify completeness and schema consistency. Transformation checks (dbt tests) verify row counts, null handling, uniqueness, and business rule correctness. Loading checks verify that destination data matches source totals. Monitoring tracks data freshness, volume trends, and anomalies over time. If revenue suddenly drops 50% in the pipeline output, you get an alert before anyone sees the dashboard.
Ready to get a quote on your etl pipelines and data integration?
Tell us what you are building and we will put together a scoped proposal within 3 business days. Here is what happens when you reach out:
- 1You fill in the short project brief form (takes 5 minutes).
- 2We review it and come back with initial thoughts within 24 hours.
- 3We schedule a 30 minute call to align on scope, timeline, and budget.
- 4You receive a written proposal with fixed price options.
No commitment required until you are ready. Request your free etl pipelines and data integration quote now.
Ready to start your next project?
Join over 4,000+ startups already growing with our engineering and design expertise.
Trusted by innovative teams everywhere
