How do you handle source system schema changes without breaking downstream pipelines?

Schema change handling is designed into the pipeline architecture, not bolted on after a break. For ELT pipelines, raw data lands in the warehouse in its source form -- when a source adds a column, the raw table gains a column and the transformation models decide whether to use it. For ETL pipelines, we build schema validation checks that alert when a source deviates from expected structure before the load runs. Critical pipelines include automated schema drift detection so the team knows before a report breaks.

Should we use a managed ETL tool like Fivetran or Airbyte, or build custom pipelines?

Managed ETL tools (Fivetran, Airbyte) are the right starting point when your source systems are standard SaaS tools with supported connectors and your transformation requirements are straightforward. They handle connector maintenance and scheduling so you don't have to. Custom pipeline development makes sense when your source systems are custom databases or internal APIs without supported connectors, when data volume or transformation complexity exceeds what managed tools handle economically, or when you have compliance requirements that restrict data passing through third-party infrastructure. We'll give you an honest assessment of which approach fits before scoping anything.

How long does ETL pipeline development take?

A pipeline connecting 3 to 5 source systems with standard transformations and warehouse delivery typically takes 6 to 10 weeks. A more complex build with custom database connectors, CDC replication, and a full dbt transformation layer typically takes 10 to 16 weeks. Timeline depends on the number of sources, data quality issues in those sources, and transformation complexity. We assess all three during a scoping phase before committing to a timeline.

What happens when a source system is unavailable and the pipeline fails to extract?

Pipeline failure handling is a first-class part of the design. When extraction from a source fails, the pipeline logs the failure, skips the load step (preserving the last successful data state in the warehouse), and triggers an alert. Partial loads -- where some records extracted before a failure -- are handled with transactional load patterns that commit only complete batches. Retry logic with backoff handles transient source unavailability without operator intervention.

How do you handle source system schema changes without breaking downstream pipelines?

Schema change handling is designed into the pipeline architecture, not bolted on after a break. For ELT pipelines, raw data lands in the warehouse in its source form -- when a source adds a column, the raw table gains a column and the transformation models decide whether to use it. For ETL pipelines, we build schema validation checks that alert when a source deviates from expected structure before the load runs. Critical pipelines include automated schema drift detection so the team knows before a report breaks.

Should we use a managed ETL tool like Fivetran or Airbyte, or build custom pipelines?

Managed ETL tools (Fivetran, Airbyte) are the right starting point when your source systems are standard SaaS tools with supported connectors and your transformation requirements are straightforward. They handle connector maintenance and scheduling so you don't have to. Custom pipeline development makes sense when your source systems are custom databases or internal APIs without supported connectors, when data volume or transformation complexity exceeds what managed tools handle economically, or when you have compliance requirements that restrict data passing through third-party infrastructure. We'll give you an honest assessment of which approach fits before scoping anything.

How long does ETL pipeline development take?

A pipeline connecting 3 to 5 source systems with standard transformations and warehouse delivery typically takes 6 to 10 weeks. A more complex build with custom database connectors, CDC replication, and a full dbt transformation layer typically takes 10 to 16 weeks. Timeline depends on the number of sources, data quality issues in those sources, and transformation complexity. We assess all three during a scoping phase before committing to a timeline.

What happens when a source system is unavailable and the pipeline fails to extract?

Pipeline failure handling is a first-class part of the design. When extraction from a source fails, the pipeline logs the failure, skips the load step (preserving the last successful data state in the warehouse), and triggers an alert. Partial loads -- where some records extracted before a failure -- are handled with transactional load patterns that commit only complete batches. Retry logic with backoff handles transient source unavailability without operator intervention.

How many hours per week does someone spend manually exporting data from one system and importing it into another?
When a source system changes its schema, does your pipeline fail silently or alert you before bad data reaches reports?

Data that lives in source systems is not data you can use. ETL pipelines are what move it to where decisions get made.

ETL (extract, transform, load) and ELT (extract, load, transform) pipelines are the infrastructure that connects source systems -- ERP, CRM, WMS, SaaS tools, databases -- to your data warehouse or analytics layer. Without them, every report is a manual export and every business question requires someone to spend two days joining spreadsheets.

We design and build ETL/ELT pipelines that run on a defined schedule, handle source system changes gracefully, and deliver clean, consistent data to your warehouse or downstream consumers. Architecture, development, testing, and monitoring -- scoped and priced as one engagement.

Batch ETL pipelines connecting ERP, CRM, SaaS tools, and custom databases
ELT architecture on Snowflake, BigQuery, or Redshift -- raw data preserved, transformations versioned
Incremental load patterns that move only changed records rather than full table dumps
Pipeline monitoring with alerting when source schema changes or row counts deviate from expected

RaftLabs builds ETL and ELT pipelines that connect source systems -- ERP, CRM, SaaS tools, and databases -- to data warehouses on Snowflake, BigQuery, or Redshift. Batch and incremental pipelines, schema change handling, and pipeline monitoring. Most pipeline projects deliver in 6 to 12 weeks at a fixed cost.

ETL and ELT pipelines are the infrastructure layer between your source systems and every report, dashboard, or ML model that depends on that data. Without a pipeline layer, analysts pull manual exports, join spreadsheets, and build one-off scripts that break when a source system changes. The result is reporting that lags, numbers that don't reconcile, and a data team that spends most of its time on data preparation rather than analysis.

Building that pipeline layer is an engineering project with real architecture decisions: which extraction pattern fits each source, how transformations are versioned and tested, what happens when a source is unavailable, and how the team gets alerted when something goes wrong. We scope that as a single engagement -- architecture, development, testing, and monitoring -- and deliver it at a fixed cost agreed before development starts.

What we build

Batch ETL pipeline development

Scheduled extraction from ERP, CRM, WMS, databases, and SaaS APIs. Full-table and incremental load patterns -- incremental loads move only changed records rather than re-extracting the entire table on every run. Transformation applied before load for ETL patterns. Schedule management, error handling, and retry logic built into the pipeline so manual intervention is the exception, not the routine. Source systems covered include SAP, Oracle ERP, Salesforce, HubSpot, and custom internal databases.

ELT architecture on cloud warehouses

Raw data landing in Snowflake, BigQuery, or Redshift before transformation runs in the warehouse. dbt-based transformation layer with SQL models checked into version control. Incremental materialisation strategies that reduce warehouse compute cost on large tables. Schema evolution handling so that new columns in the source appear in the raw layer automatically. Transformation logic separated from extraction logic so each can be changed independently without touching the other.

SaaS and API data ingestion

Connectors for Salesforce, HubSpot, Stripe, Shopify, Google Ads, and custom REST or GraphQL APIs. Rate limit handling, pagination, and authentication token refresh built into each connector. Incremental pull by updated_at timestamp or cursor so each run fetches only records changed since the last run. Response normalisation to a consistent warehouse schema regardless of how the source API structures its payload. New connector development for internal APIs without third-party support.

Database replication and CDC

Change Data Capture from MySQL, PostgreSQL, SQL Server, and Oracle. Binlog-based CDC captures inserts, updates, and deletes at the row level without polling -- no full table scans, no load on the production database beyond reading the transaction log. Real-time or near-real-time replication to the warehouse or data lake. CDC is the right pattern when you need to capture deletes, track row history, or replicate tables too large to re-extract on each run.

Pipeline monitoring and alerting

Run logging, row count validation, schema change detection, and freshness SLA monitoring across all pipeline jobs. Alerting to Slack or email when pipelines fail, when delivered record counts fall outside expected ranges, or when a source schema changes in a way that affects downstream tables. Central dashboard for pipeline health across all jobs -- run status, last success time, record counts, and alert history in one view for the data team.

Data transformation and modelling

dbt models for cleaned, business-defined entities: customers, orders, revenue, product usage, and any domain-specific constructs your reporting requires. Staging, intermediate, and mart layers that separate raw source data from business logic so each layer can be changed without cascading failures. Documentation generated from the model definitions so analysts understand what each field means and where it comes from. Built-in tests for null values, referential integrity, uniqueness, and custom business logic run on every pipeline execution.

Have a data pipeline project?

Tell us your source systems, what data you need to move, and what breaks today when a pipeline fails. We'll scope the architecture and give you a fixed cost.

Talk about your data pipeline project

Data Engineering Services -- full data engineering capability overview
Data Warehouse Development -- warehouse design and build on Snowflake, BigQuery, and Redshift
Real-Time Data Pipelines -- streaming pipelines for data that needs to be current
Data Quality Management -- validation, monitoring, and anomaly detection

Predictive Analytics -- ML models built on top of your cleaned data layer
Business Intelligence -- dashboards and reporting on top of your data warehouse
Cloud Migration -- move your databases and data infrastructure to cloud

Frequently asked questions

: Schema change handling is designed into the pipeline architecture, not bolted on after a break. For ELT pipelines, raw data lands in the warehouse in its source form -- when a source adds a column, the raw table gains a column and the transformation models decide whether to use it. For ETL pipelines, we build schema validation checks that alert when a source deviates from expected structure before the load runs. Critical pipelines include automated schema drift detection so the team knows before a report breaks.
: Managed ETL tools (Fivetran, Airbyte) are the right starting point when your source systems are standard SaaS tools with supported connectors and your transformation requirements are straightforward. They handle connector maintenance and scheduling so you don't have to. Custom pipeline development makes sense when your source systems are custom databases or internal APIs without supported connectors, when data volume or transformation complexity exceeds what managed tools handle economically, or when you have compliance requirements that restrict data passing through third-party infrastructure. We'll give you an honest assessment of which approach fits before scoping anything.
: A pipeline connecting 3 to 5 source systems with standard transformations and warehouse delivery typically takes 6 to 10 weeks. A more complex build with custom database connectors, CDC replication, and a full dbt transformation layer typically takes 10 to 16 weeks. Timeline depends on the number of sources, data quality issues in those sources, and transformation complexity. We assess all three during a scoping phase before committing to a timeline.
: Pipeline failure handling is a first-class part of the design. When extraction from a source fails, the pipeline logs the failure, skips the load step (preserving the last successful data state in the warehouse), and triggers an alert. Partial loads -- where some records extracted before a failure -- are handled with transactional load patterns that commit only complete batches. Retry logic with backoff handles transient source unavailability without operator intervention.