Build Scalable, Secure Data Pipelines in the Cloud
AWS Data Engineering by AMSYS delivers end‑to‑end data ingestion, processing, and storage using AWS services like Glue, Kinesis, EMR, and Redshift. With AMSYS’s proven framework, you’ll accelerate insights, optimize costs, and enforce enterprise‑grade governance across your data ecosystem.
What is AWS Data Engineering?
AWS Data Engineering is the practice of designing and operating data pipelines on AWS using managed services—Glue, Kinesis, EMR, Redshift, S3—to ingest, transform, and store data at any scale. AMSYS architects robust AWS solutions, automates workflows, and applies best practices to ensure performance, reliability, and cost efficiency.
Eliminate bottlenecks, silos, and risk with AMSYS expertise.
Manually integrating on‑premises, cloud, and SaaS data slows projects and introduces errors.
Traditional ETL tools struggle to handle petabytes of data cost‑effectively and at speed.
Meeting low‑latency streaming requirements demands a scalable, managed platform.
Ensuring lineage, security, and auditability across dynamic pipelines is complex without automation.
Leverage AWS managed services for agility, scale, and reliability.
Fully managed Spark-based ETL that auto‑scales and minimizes operational overhead.
Kinesis Data Streams & Firehose capture and deliver data with millisecond latency.
EMR and Glue handle large‑scale Spark, Hadoop, and Presto workloads on demand.
Cost‑effective, durable object storage with Lake Formation for fine‑grained access control.
Redshift and Redshift Spectrum for petabyte‑scale analytics with high concurrency.
Drive faster insights, lower TCO, and strengthen governance.
Structured approach to design, build, and operate your data platform.
Assess your data estate, define use cases, and craft an AWS migration roadmap.
Design scalable, secure AWS data architectures with best‑practice patterns and IaC.
Develop Glue jobs, Kinesis streams, EMR clusters, and orchestrate with Step Functions.
Automate unit and integration tests to ensure data accuracy, performance, and SLA compliance.
24/7 AMSYS monitoring, incident response, and continuous optimization to keep pipelines running smoothly.
Guidelines to maximize performance, security, and maintainability.
Use CloudFormation or Terraform for repeatable, auditable deployments.
Break monolithic jobs into reusable, parameterized components.
Set up Cost Explorer alerts, use spot instances, and right‑size clusters.
Enforce least‑privilege IAM, encrypt data at rest and in transit, and audit with CloudTrail.
Review metrics, tune configurations, and adopt new AWS features regularly.
Efficiently bring data into your AWS environment.
Capture, buffer, and deliver streaming data to S3, Redshift, or Elasticsearch.
Transform and enrich data at scale.
Serverless Spark-based jobs with built-in transforms and libraries.
Managed Hadoop and Spark clusters for heavy‑duty processing.
Event-driven, micro‑batch, or real‑time transformations with minimal provisioning.
Visual, no‑code data cleaning and profiling for business analysts.
Organize data for analytics and machine learning.
Durable, cost‑effective object store with lifecycle policies and tiering.
Centralized data catalog and fine‑grained access control for your data lake.
Low‑latency NoSQL and relational storage for operational workloads.
Protect data and ensure compliance across pipelines.
AWS IAM & KMS
Define least‑privilege roles and encrypt data at rest and in transit.
AWS CloudTrail & Config
Audit all API calls and track resource configuration changes for compliance.
Lake Formation Permissions
Manage table‑ and column‑level access controls in your data lake.
AWS Security Hub
Centralize security findings and automate remediation workflows.
