Data pipeline engineering that turns scattered data into clean, AI-ready data with EdgeFirm

Common Questions About Data Pipelines

How is this different from just using Fivetran or Airbyte?

Fivetran and Airbyte are great for extraction (the 'E' in ETL), and we often use them. But they don't solve the hard problems. These are data modeling (how do you calculate MRR?), quality monitoring (is the data correct?), transformation logic (business rules), and links to your analytics tools. We build the whole data platform, not just the connectors. We also handle sources these tools don't support and build custom transformations for your own rules.

What data sources can you connect to?

Almost anything. That includes SaaS apps (Salesforce, HubSpot, Shopify, Stripe, and more), databases (PostgreSQL, MySQL, MongoDB, SQL Server, Oracle), files (CSV, Excel, JSON from SFTP, S3, email), streaming data (Kafka, webhooks, CDC), and even legacy systems like mainframes and on-premise databases behind firewalls. If it has an API or can export data, we can connect it.

How do you handle data quality issues?

Data quality is built into every layer. During ingestion: Schema validation, freshness checks, row count monitoring. During transformation: Deduplication, standard formats, null handling, business rule checks. Post-load: Auto tests, anomaly checks, data profiling. We use Great Expectations and dbt tests to catch issues before they reach dashboards. You get Slack alerts when something's wrong, and dashboards that show data health.

Can this work with our existing data warehouse?

Yes. If you already have Snowflake, BigQuery, Redshift, or another warehouse, we build on top of it. We'll review your setup, suggest fixes, and add new pipelines next to the old ones. We can also help you move from one warehouse to another if needed. Our transformations are portable SQL/dbt, so you're not locked into any vendor.

What about real-time data needs?

We support several latency tiers. Batch (hourly/daily) suits most analytics use cases and is the simplest and cheapest option. Near real-time (5-15 minutes) uses streaming ingestion and micro-batching. True real-time (seconds) uses Kafka, change data capture (CDC), and streaming transformations. Most clients find that near real-time is enough. Only a few metrics need sub-minute latency. We'll help you decide what's needed and what's nice to have.

How do you ensure data security?

Security is built in from day one. Infrastructure: Encryption at rest and in transit, VPC isolation, IAM roles with least privilege. Access Control: Role-based access, column-level security for PII, row-level security for multi-tenant data. Audit: Full logs of who accessed what, and data lineage for compliance. Compliance: designed with SOC 2 guidance, HIPAA-aligned safeguards with a signed BAA, and GDPR support for data locality and deletion. We work within your security requirements and can deploy in your cloud account.

What ongoing maintenance is required?

We build for minimal maintenance. Pipelines are self-healing with automatic retries. Schema drift checks catch source changes before they break things. Alerts reach you only when a person must step in. Typical ongoing work: Adding new data sources (we document how, or you can hire us). Updating transformations when business rules change. Responding to alerts (most fix themselves). Most clients manage this with their existing team, or we offer retainer support for hands-off operation. Retainer terms are agreed with you individually during scoping, based on your actual support needs.

How much does a data pipeline project cost and how long does it take?

Projects start from $20K at a fixed price, and a full build is delivered in 4 to 5 months. Price depends on the number of sources (5-10 versus 20+ systems), data volume and speed (GB versus TB, batch versus real-time), how complex the transformations are, and compliance requirements like PII handling or HIPAA. A full build includes extract pipelines for 5-10 priority sources, the full transformation and quality framework, BI integration, and 30 days of post-launch support.

What's the difference between this and Decision Intelligence & Analytics?

Data pipeline engineering is the plumbing: extracting, cleaning, and loading your data into one warehouse with quality checks. Decision Intelligence & Analytics is the layer on top, the plain-language queries, dashboards, and forecasts that sit on data already assumed clean. Most clients need the pipeline first. If your data is already in good shape in a warehouse, you can start with the analytics layer.

Do we have to migrate to a specific warehouse like Snowflake or BigQuery?

No. We support Snowflake, BigQuery, Redshift, Azure Synapse, and PostgreSQL, and our transformations are portable SQL/dbt, so you are not tied to one vendor. If you already have a warehouse, we build on it. If you don't have one yet, we'll suggest one based on your volume, team skills, and budget, not a fixed default.

How will we know if something breaks?

Monitoring runs on every pipeline. Freshness checks catch data that stopped updating, volume anomaly detection flags unexpected drops or spikes, and schema drift detection catches source changes before they reach your dashboards. Alerts go to Slack or email with severity levels. Short failures retry on their own, so most issues fix themselves before anyone looks.