AWS Data Analytics Services

Turn AWS data into clear decisions with trusted metrics and analytics built around your business. Our AWS data analytics services combine data engineering, BI, and cloud expertise to help teams get more value from the data they already have and move from silos to informed decisions.

Talk to an AWS analytics specialist

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Why Teams Choose AWS

AWS gives teams a practical way to bring scattered data together, work with it at scale, and turn it into insights people can actually use. For organizations using AWS for analytics, the right setup can make data easier to trust, explore, and act on as needs grow.

  • Centralize data in lakes and warehouses

    Bring raw and curated data into a more consistent environment with Amazon S3 and Amazon Redshift. Lifecycle policies, tiered storage, and clear data structures can make large datasets easier to manage and query over time.

  • Use AWS real-time analytics for streaming data

    AWS real-time analytics can help teams respond to events as they happen. Amazon Kinesis and AWS Lambda can ingest and process streaming data for dashboards, alerts, and other time-sensitive workflows.

  • Add predictive insights where they matter

    Services such as Amazon SageMaker can extend analytics with forecasting, anomaly detection, or other machine learning use cases. The value comes from applying models where they support a clear business or operational decision.

  • Scale across teams and use cases

    Serverless and elastic AWS services let analytics environments grow with new data sources, teams, or product areas. Infrastructure as code can also make changes easier to manage consistently as the setup becomes more complex.

  • Strengthen security and data governance

    IAM, encryption, CloudTrail, and clear access policies can support stronger control over sensitive data and changes to the environment. Combined with defined ownership and governance practices, they give teams better visibility into how data is accessed and managed.

  • Keep cloud spend visible

    Analytics workloads can become expensive as usage grows, so cost management needs to be part of the architecture. Right-sizing, storage tiers, workload scheduling, and usage monitoring can help teams understand and control spend more effectively.

See where AWS analytics can create the most value for your data environment:

Our AWS Analytics Services

Build a reliable analytics backbone on AWS. We design and implement data ecosystems your teams can understand, use, and evolve with confidence. Our AWS data analytics consulting services can support a defined project or longer-term collaboration, depending on your architecture and goals.

  • Data Strategy and Roadmap

    Set direction before you start building. We assess business goals, current data flows, risks, and priorities, then shape a practical roadmap with phased milestones, governance considerations, and relevant KPIs.

  • Data Ingestion and Pipeline Automation

    Keep data fresh and trustworthy. Our engineers design ETL/ELT pipelines for batch and streaming data using services such as AWS Glue, Amazon Kinesis, and Amazon EMR, with testing, lineage, and monitoring shaped around the reliability your workflows require.

  • Data Lake and Warehouse Setup

    Create durable storage and fast analytics in one environment. We can build Amazon S3–based data lakes, organize governed catalogs, and model curated data in Amazon Redshift for AWS big data analytics, balancing query performance, maintainability, and cost.

  • Real-Time and Predictive Analytics

    Turn incoming data into useful signals sooner. Streaming pipelines with Amazon Kinesis and AWS Lambda can support timely alerts, while predictive models add forecasting or anomaly detection where they contribute to real decisions.

  • BI Dashboards and Data Visualization

    Give teams the views they actually need. We build Amazon QuickSight dashboards and self-service BI experiences around trusted datasets, with access controls, refresh schedules, and documentation that make them easier to use and maintain.

  • Data Governance and Compliance Advisory

    Make data ownership, access, and retention easier to manage as the analytics environment grows. Beetroot engineers can configure IAM, encryption, logging, and data governance controls while supporting the documentation and technical evidence your internal security and compliance teams need.

  • Ongoing Support and Scale-Out

    As data volumes, teams, or reporting needs grow, the analytics environment may need to evolve with them. Where ongoing support is part of the engagement, our engineers can handle fixes, capacity planning, enhancements, and versioned changes across analytics services in AWS.

  • Analytics Optimization & Cost Monitoring

    Keep performance and cloud spend visible as usage changes. We can review queries, storage patterns, compute usage, and workload scheduling, then identify practical opportunities to improve performance or reduce unnecessary cost.

  • Shape your AWS analytics setup around the data and decisions that matter most:

AWS Analytics Technologies We Use

During discovery, we select the right AWS analytics service for each stage, from data ingestion and processing to BI and machine learning. Together, these services form an AWS cloud analytics stack that can grow with your needs, with cloud optimization services available for projects that require performance or cost tuning.

  • Data Lakes & Catalogs

    Amazon S3
    AWS Lake Formation
    AWS Glue Data Catalog
    Amazon S3 Glacier

  • Data Warehousing & SQL Analytics

    Amazon Redshift
    Amazon Redshift Serverless
    Amazon Athena
    Redshift Spectrum

  • Processing & Orchestration

    AWS Glue
    Amazon EMR
    AWS Lambda
    AWS Step Functions

  • Security, Governance & Cost Management

    AWS IAM
    AWS KMS
    AWS CloudTrail
    AWS Cost Explorer

Cooperation Models

Choose how you want to work with us on data analytics on AWS — through embedded expertise, a defined project, or focused team workshops. Each model can adapt to your roadmap while keeping priorities and ownership clear.

  • Dedicated Development Teams

    Extend your team with AWS data engineers, analytics architects, and DevOps specialists who work within your existing processes. You set priorities and technical standards, while Beetroot handles team setup and administrative support.

  • Milestone-Based Solutions

    Engage a managed team for a defined outcome, such as a Redshift warehouse, a streaming pipeline, or a BI rollout. We plan, execute, and report on the agreed scope, while you maintain strategic oversight.

  • Custom Tech Training

    Need specific skills for your existing team? Our instructors can design tailored 1–3-day workshops around your stack and current challenges. The focus is on practical exercises your team can then apply in daily work.

Choose the level of support that fits your AWS analytics roadmap

AWS Analytics Consultants for Hire

Need additional AWS analytics expertise? We connect you with vetted data engineers and analytics specialists matched to your stack and project needs. They can join your team for ongoing work or support a defined analytics initiative.

  • $44

    Information Security Engineer

    Maria L., 5+ years of experience
    Skilled in network standards (TCP/IP, OSI), *NIX systems (Linux, BSD), coding in C++, Java, Python, Bash, and reverse engineering (IDA, Jadx), with expertise in application testing standards (OWASP). Experience includes penetration testing, security audits, OSINT, vulnerability identification, SOC monitoring, and incident response.
    • Cloud Platforms: AWS, Azure, GCP
    • DevOps

    Request full CV

  • $65/h

    Cloud Developer

    Tetiana K., Infrastructure Automation, 8+ years of experience
    Specializes in Google Cloud Platform and AWS with extensive experience in Infrastructure as Code using Terraform and CloudFormation. Skilled in building reliable and secure cloud environments, optimizing cloud costs, and implementing automated deployment workflows. She’s excellent at setting up infrastructure automation from scratch.
    • CI/CD
    • Cloud Platforms: AWS, Azure, GCP
    • IaC/Config: Terraform, CloudFormation (IaC), Ansible
    • Jenkins / GitLab CI / GitHub Actions / Git
    • Orchestration: Kubernetes, Docker
    • Python
    • Serverless Framework, AWS Lambda / GCP Cloud Functions

    Request full CV

  • $80/h

    Senior Cloud Engineer

    Serhii L., DevSecOps, 12+ years of experience
    Proficient in AWS and Azure cloud platforms with a strong background in implementing security best practices and automating cloud infrastructure. Experienced in Python scripting, CI/CD pipelines, and container orchestration, delivering scalable solutions for complex enterprise environments.
    • Cloud Platforms: AWS, Azure, GCP
    • HashiCorp Vault
    • IaC/Config: Terraform, CloudFormation (IaC), Ansible
    • Jenkins / GitLab CI / GitHub Actions / Git
    • Orchestration: Kubernetes, Docker
    • Prometheus / Grafana / ELK Stack / Google Cloud operations
    • Python

    Request full CV

  • $75/h

    Cloud Engineer

    Volodymyr H., DevSecOps, 10+ years of experience
    Skilled in AWS cloud technologies with a strong focus on cloud security, Python programming, and the administration of AWS accounts, contributing to safeguarding critical infrastructures while seeking new opportunities for growth in a collaborative and transparent environment.
    • Cloud Platforms: AWS, Azure, GCP
    • HashiCorp Vault
    • IaC/Config: Terraform, CloudFormation (IaC), Ansible
    • Jenkins / GitLab CI / GitHub Actions / Git
    • Orchestration: Kubernetes, Docker
    • Prometheus / Grafana / ELK Stack / Google Cloud operations
    • Python
    • Serverless Framework, AWS Lambda / GCP Cloud Functions

    Request full CV

  • $67/h

    Cloud Engineer

    Adam D., DevSecOps, 10+ years of experience
    Skilled in AWS cloud technologies with a strong focus on cloud security, Python programming, and the administration of AWS accounts, contributing to safeguarding critical infrastructures while seeking new opportunities for growth in a collaborative and transparent environment.
    • Cloud Platforms: AWS, Azure, GCP
    • DevOps

    Request full CV

AWS Data Analytics Strategy

Turn raw data into trusted decisions with a clear plan. We start with your current data environment, choose AWS services around the use case, and build the pipelines, models, and dashboards needed to put that plan into practice.

  • Discovery & Audit

    Step 1

    Inventory data sources, owners, SLAs, and quality issues, then map access, costs, risks, and business priorities. The findings shape the scope, KPIs, and a realistic delivery plan.

  • Architecture Design & Tool Selection

    Step 2

    Select architecture patterns and AWS services that fit your goals, using tools such as Amazon S3, Amazon Redshift, AWS Glue, Amazon Kinesis, Amazon Athena, and Amazon QuickSight. Security, data governance, and cost considerations are built into the design from the start.

  • Pipeline Setup & Integration

    Step 3

    Build ETL/ELT pipelines for batch and streaming data, with validation, schema handling, and alerts around data ingestion. Source systems and targets are connected so information can move through the analytics environment consistently.

  • Model Deployment (Predictive or BI)

    Step 4

    Deploy model endpoints where forecasting or other predictive use cases add value, or prepare curated datasets and dashboards for business intelligence (BI). Documentation gives teams a clearer view of how outputs fit into day-to-day decisions.

  • Testing & Validation

    Step 5

    Our engineers add data quality checks, unit tests, and query benchmarks, while permissions and encryption are reviewed before release. Outputs are also checked against agreed expectations and business rules.

  • Monitoring & Optimization

    Step 6

    Build ETL/ELT pipelines for batch and streaming data, with validation, schema handling, and alerts around data ingestion. Connect source systems and targets so information can move through the analytics environment consistently.

AWS Predictive Analytics For Key Industries

Different industries ask different questions of their data. The right AWS analytics setup can turn operational, customer, and product data into clearer signals for planning, monitoring, and day-to-day decisions.

  • HealthTech

    Connect EHR, device, and telemedicine data in governed analytics environments that support clinical, research, and operational teams. Encryption, role-based access, and audit trails can help protect sensitive information while dashboards make key trends easier to follow.

  • GreenTech

    Combine sensor data from energy, forestry, and climate systems for monitoring, forecasting, and anomaly detection. Streaming pipelines and dashboards can help teams track changing conditions, while thoughtful storage and partitioning keep growing datasets manageable.

  • FinTech

    Consolidate transactions, risk signals, and operational data in governed analytics environments. Real-time dashboards and models can support fraud monitoring, liquidity analysis, and other decisions, with access controls, encryption, and lineage built around sensitive financial data.

  • EdTech

    Unify LMS, content, and engagement data into curated datasets for teachers, administrators, and product teams. Dashboards and event pipelines can support progress monitoring, platform health, and analysis during periods of higher demand.

  • E-Commerce & Retail

    Blend clickstream, catalog, pricing, and inventory data for a clearer view of customer and sales activity. Forecasting, anomaly detection, and near-real-time reporting can support demand planning, campaign analysis, and fulfillment decisions as conditions change.

  • Logistics & Mobility

    Stream telematics, orders, and operational signals into dashboards that surface delays, exceptions, and SLA risks. Predictive models can support ETA and capacity planning, while historical data gives teams a stronger basis for network and pricing decisions.

Why Beetroot for AWS Business Analytics

Good AWS business analytics depends on more than choosing the right cloud services. Beetroot combines data and AWS expertise with clear communication, flexible delivery, and a practical approach to building analytics your team can understand and evolve.

  • AWS-Certified Expertise

    Work with AWS-certified professionals who understand the data engineering, architecture, and cloud decisions behind production analytics. Documentation and knowledge transfer keep your team close to the system as it develops.

  • Experience Across Data-Heavy Domains

    HealthTech, FinTech, GreenTech, and EdTech bring different expectations around data quality, security, latency, and governance. Our experience across these domains helps us ask the right questions early and adapt the technical approach to the context.

  • Delivery That Fits Your Team

    Choose a defined project or extend your existing team with AWS and data specialists. You retain strategic ownership, while the delivery setup can adapt as priorities and scope change.

  • Transparent, Sustainable Collaboration

    We’re open about scope, costs, and technical trade-offs as the work progresses. Efficiency matters too — from avoiding unnecessary cloud spend to building systems that are practical for the people who will maintain them.

  • Security and Compliance by Design

    IAM, encryption, logging, and governance requirements can be considered from the start rather than added later. For regulated environments, our engineers can also support the controls and documentation your internal security and compliance teams need for review.

  • Long-Term Support

    When ongoing support is included in the engagement, our engineers can help troubleshoot issues, share practical guidance, and transfer knowledge so your team can manage the environment with confidence.

Client Testimonials

Some of our work stays behind NDAs, but the feedback we can share spans data, cloud, product, and long-term engineering partnerships. Here’s what clients say about working with Beetroot.

  • Adam Wamai Egesa,
    Co-Founder and CTO at Normative

    Beetroot’s breadth of knowledge on different frameworks and technologies is impressive. We were using a somewhat niche or rare framework, and were struggling to find people who even knew about it. Beetroot developers were acquainted with it and easily accomplished tech tasks of all complexity.

Featured Cases

Here are a few examples of how we’ve worked with clients on data, cloud, AI, and product development projects.

  • Land Life

    Beetroot supported Land Life with computer vision and data processing for large volumes of drone imagery used in reforestation projects. The solution turns complex geospatial data into practical insights that help teams assess tree health and monitor restoration work.

    Read the full story

    • Python
    • AWS
    • MongoDB
    • Machine Learning
    • QGIS

Custom Tech Workshops for Teams

Give your team focused time with experienced practitioners to build the skills they need for the work ahead. Each workshop is tailored to your goals, existing knowledge, and technology setup, with the content shaped around real challenges rather than a fixed curriculum.

  • Build Confidence with Analytics on AWS

    Deepen your team’s understanding of Analytics on AWS, from data pipelines and storage to BI, governance, and cloud cost considerations.

  • Focus on the AWS Skills You Actually Need

    Shape the agenda around your stack and current challenges, whether that means Amazon S3 and Redshift, Kinesis and Glue, QuickSight, or broader architecture decisions.

  • Apply New Knowledge to Real Work

    Hands-on exercises and instructor feedback keep the training connected to practical scenarios, giving your team approaches they can carry into ongoing AWS analytics work.

Start Your AWS Analytics Project:

Have an AWS analytics challenge in mind? Tell us what you’re trying to improve, and we’ll help you explore the right solution for your data and priorities.

    FAQs

    Planning AWS analytics usually starts with practical questions about architecture, timelines, cost, and security.

    Nick Tykhomyrov, CBDO, Beetroot

    Nick Tykhomyrov

    CBDO