AWS Data & Analytics
Most organizations have more data than they can use. The problem isn't collection — it's building the architecture that makes data available, queryable, and trustworthy so the people who need it can act on it.
EFS Networks designs and builds data platforms on AWS that cover the full stack: ingestion from operational systems, transformation pipelines, a queryable data lake or lakehouse, and the reporting layer your business users actually open every morning. We treat data infrastructure as a product, not a project — which means it doesn't collapse when someone changes an upstream schema or adds a new data source.
For teams where analytics is leading toward predictive modeling or machine learning, we work alongside our EFS AI practice to extend data platforms into ML feature stores and inference pipelines — so the same data that feeds your CFO's dashboard can feed your demand forecasting model.
What We Deliver
- Data lake architecture on S3 — Zone design (raw, curated, consumption), Lake Formation governance and fine-grained access control, partition strategies for cost-efficient Athena queries, and lifecycle policies that keep storage costs predictable as data volumes grow.
- Glue ETL & ELT pipelines — Glue jobs for batch transformation, Glue Data Catalog for schema management, CDC pipeline design for near-real-time sync from operational databases (RDS, Aurora, DynamoDB Streams), and DQ validation built into the pipeline — not bolted on after bad data reaches reporting.
- Athena query optimization — Columnar format conversion (Parquet/ORC), partition pruning, workgroup cost controls, and federated query configuration for cross-source joins without moving data.
- Redshift warehouse design — Distribution key and sort key strategy for your actual query patterns, Redshift Serverless vs. provisioned trade-off analysis, Spectrum for S3-resident data, and RA3 node migration planning for existing clusters.
- QuickSight dashboards & SPICE — Dataset design, SPICE capacity planning, row-level security for multi-tenant reporting, calculated fields, and embedding dashboards into internal applications.
- Lakehouse with AWS Glue + Iceberg — Apache Iceberg table format on S3 for ACID transactions, time-travel queries, and schema evolution without full table rewrites — the pattern that eliminates the "we can't change this table because everything breaks" problem.
- Pipeline orchestration — Step Functions for multi-stage ETL orchestration with retry logic, error alerting, and dependency management. EventBridge Scheduler for time-based triggers.
- Data mesh enablement — Domain-oriented data ownership patterns using Lake Formation cross-account sharing, so business units own their data products without every analyst hitting the central team as a bottleneck.
Delivered Outcomes
- ✓ Clients have achieved 99.5%+ pipeline reliability after implementing schema validation, DQ checks, and automated retry logic
- ✓ Estimated 60–80% query performance improvement on Athena after Parquet conversion and partition strategy redesign
- ✓ Estimated 50–70% total cost of ownership reduction versus on-premises data warehouses (Teradata, Netezza)
- ✓ Time-to-insight from days (manual extraction requests) to self-service — business users query current data in QuickSight without filing a ticket
Frequently Asked Questions
What is the difference between a data lake and a data warehouse on AWS?
A data lake (S3-based) stores raw data in any format at low cost, queried via Athena or Spark. A data warehouse (Redshift) stores structured, transformed data optimized for fast analytical queries. Most modern architectures use both — a lakehouse pattern with Apache Iceberg provides the best of both: S3 storage costs with warehouse-grade query performance.
How does AWS Glue ETL work?
AWS Glue runs serverless Spark jobs that extract data from sources (databases, APIs, files), transform it (clean, enrich, aggregate), and load it into your data lake or warehouse. The Glue Data Catalog provides a central schema registry. We build Glue pipelines with schema validation and data quality checks built in — not bolted on after bad data reaches reporting.
Should I use Athena or Redshift for analytics?
Athena is ideal for ad-hoc queries on S3 data — pay-per-query, no infrastructure to manage. Redshift is better for heavy, repetitive analytical workloads where query performance and concurrency matter. For most mid-market organizations, we start with Athena and move to Redshift when query patterns and volumes justify it.
Outcomes are from client engagements. Actual results vary based on environment, scope, and organizational context.
Let's talk about what you're building.
Our team brings over two decades of experience to every engagement. Tell us about your project and we'll show you what's possible.