Data Analytics, Lakehouse Engineering and Real Time Pipelines on AWS

Enterprises want to use data for decision making yet face challenges such as fragmented data sources, legacy ETL systems, inconsistent governance, poor performance, high costs and limited trust in analytics outputs. Goaltech solves these challenges by building AWS native data platforms that unify ingestion, transformation, storage, governance, cataloging, lineage, streaming analytics and BI into a secure and efficient lakehouse model. We implement modern AWS analytics patterns including data lakes with Amazon S3, lakehouse architectures powered by AWS Glue Catalog and Apache Iceberg, data warehousing with Amazon Redshift Serverless, low latency analytics with Kinesis and MSK and governed metadata layers with Lake Formation. Our implementations follow the AWS Well Architected Analytics Lens and incorporate FinOps, observability and multi account governance from day one. WHAT WE DELIVER

Modern data lake and lakehouse architecture -Amazon S3 based data lake with secure, scalable storage layers -Apache Iceberg lakehouse architecture for ACID transactions, schema evolution and time travel -Glue Catalog integration for metadata, schema governance and interoperability -Partitioning, compaction, file size optimization and Parquet or ORC best practices -Cross account data sharing with Lake Formation for groups, holdings or multi agency environments -Cloud data warehousing and Redshift modernization

Amazon Redshift Serverless for elastic, fully managed warehousing -Migration from legacy MPP systems or RA3 node modernization -Workload management tuning, concurrency scaling and materialized view acceleration -Redshift Spectrum integration for unified lakehouse querying -Column level and row level security aligned with governance requirements

ETL and ELT pipeline modernization -ETL and ELT pipelines using AWS Glue, Glue Workflows, Step Functions and Lambda -Glue Data Quality rules, profiling, schema drift checks and anomaly detection -dbt integration with Redshift, Athena or Iceberg tables -Structured and semi structured data ingestion pipelines from operational systems -Real time and streaming analytics

Streaming ingestion with Amazon Kinesis and Amazon MSK -MSK Connect and Schema Registry integration -Stream processing using Kinesis Data Analytics for Apache Flink -Streaming ETL with Spark Structured Streaming on EMR -Low latency operational dashboards and anomaly detection pipelines

Data governance, cataloging, security and compliance -Lake Formation based governance for tables, columns and row level security -Data masking, sensitive data classification and access policy automation -Audit trails, lineage and metadata management using Glue Catalog and OpenLineage -IAM fine grained policies, Secrets Manager, KMS and PrivateLink for secure connectivity -Compliance alignment with GDPR, KVKK, ISO 27001 and sector specific requirements

Data observability and reliability -Data pipeline observability dashboards with CloudWatch, OpenSearch or Grafana -Data quality scoring, freshness checks and anomaly detection -Operational metrics for ETL performance, cost and data SLAs -Lineage visualization and traceability from source to consumption

BI, decision intelligence and self service analytics -QuickSight dashboards with semantic models, KPI frameworks and row level security -Self service BI enablement for finance, operations, marketing and product teams -Embedded analytics for applications or portals -Governed metrics layer for consistent, organization wide definitions

ML readiness and advanced analytics integration -Feature engineering pipelines integrated with SageMaker and Feature Store -Notebook environments for exploration using SageMaker Studio -Direct integration of lakehouse and warehouse datasets with ML models -Support for ML monitoring, drift detection and automated retraining triggers

ENGAGEMENT MODEL

1. Discovery and assessment We assess your current data architecture, ingestion pipelines, governance, BI landscape and costs to define gaps and opportunities.

2. Data platform blueprint We define the target architecture for lakehouse, warehouse, ETL, streaming, governance, observability and ML integration.

3. Proof of value We implement a functional proof of value such as a streaming pipeline, Redshift dashboard or Iceberg based ETL flow.

4. Full implementation We deploy the full data platform, ingest critical sources, establish governance and build BI dashboards for priority domains.

5. Optimization and enablement We tune performance, reduce cost, enhance governance and train your teams for self service analytics and ML readiness.