Rapyder, an AWS Premier Tier Services Partner and Snowflake Premier Partner, delivers data analytics services and solutions for enterprises across AWS, Azure, Google Cloud, Databricks, and Snowflake. One data analytics company for any platform your business already runs on.
AWS Data Services Competency
Solutions Partner for Data & AI (Azure)
Google Cloud Select Services Partner
Snowflake AI Data Cloud Services Premier Partner
Databricks Partner
Overview
Data analytics services help a business collect, clean, unify and govern data from its applications, databases and devices, then turn it into dashboards, reports and AI-ready datasets that people can trust. They typically cover data engineering (pipelines, warehouses and lakehouses), data visualization and BI, real-time and IoT analytics, and data governance.
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Most enterprises don’t have a data shortage, they have a trust shortage.
That’s exactly the gap Rapyder’s data analytics services are built to close, turning scattered, stale data into a live pipeline your business can act on the moment something changes.
Services
Builds the pipelines that move, clean, and unify data from every source into one governed foundation.
Turns governed data into dashboards and reports business users actually open and trust.
Ingests and analyzes sensor and device data at scale, in real time, alongside your core business data.
Moves on-prem or aging warehouses, such as SQL Server or BigQuery, to cloud-native platforms like Amazon Redshift, Snowflake or Databricks, without breaking the reports that depend on them.
Lineage, access controls, data quality checks and cataloguing built into the pipeline, so every number in a report can be traced back to its source.
Assesses your current data estate, recommends the target architecture and platform, and builds the roadmap before anything is built, starting with a free Data Assessment.
Many of Rapyder’s data analytics services feed directly into AI and machine learning workloads, from predictive models to GenAI applications built on the same governed data foundation. See how Rapyder’s AI and Machine Learning solutions build on this same data layer.
Comparison
Both aim to inform decisions, but only one keeps up with how fast your business actually moves.
Traditional BI
Modern Data Analytics
What it does
Builds reports and dashboards from scheduled, static data pulls
Runs continuous, governed data pipelines feeding real-time dashboards and AI-ready data
Best for
Periodic reporting and historical trend review
Real-time decisions, predictive insights, and feeding ML/AI models
Data it works with
Mostly structured data, in on-prem warehouses or a single tool
Structured and unstructured data, unified in cloud-native platforms like Snowflake or Databricks
Refresh cycle
Daily, weekly, or monthly batch refreshes
Near real-time, refreshed continuously as data arrives
Scalability
Limited by on-prem hardware or a single tool’s compute
Elastic, cloud-native compute that scales on demand across AWS, Azure, or Google Cloud
Example
A monthly sales report emailed to leadership
A live dashboard flagging a revenue dip within minutes, feeding straight into forecasting
AI/GenAI readiness
Low, siloed and ungoverned
Governed, catalogued foundation with lineage and data contracts
Process
A look at how a modern data pipeline delivers an answer.
How Rapyder runs the engagement
1 · Assess
Know your data estate
2 · Design
Choose the platform
3 · Build
Deliver in increments
4 · Operate
Keep it trusted
How the pipeline delivers an answer
Use cases
Technology / SaaS
BFSI
FinTech
UserExperior: mobile app analytics and user experience monitoring platform
! PROBLEM
Needed a scalable way to ingest and visualize high-volume clickstream data, so customers could track user behaviour through their own KPIs, not generic metrics.
✓ WHAT WE DID
Rapyder built a real-time ingestion pipeline using MSK to stream clickstream data into OpenSearch, with Logstash on EC2 handling business-specific filtering and transformation, and Terraform-driven infrastructure for automated, benchmarked deployment.
RESULTS
28TBof data processed per month
✓ Millisecond-latency real-time pipeline
✓ Automated data segregation for faster queries
Nippon India Mutual Fund
! PROBLEM
Reporting relied on Oracle-based stored procedures, with high licensing costs and complex server management. Manual report generation caused delays and errors, and scaling the infrastructure to meet growing analytical needs was difficult and expensive.
✓ WHAT WE DID
Rapyder moved reporting to a serverless pipeline: Amazon Athena for queries, AWS Lambda for processing, AWS Glue for ETL, and Amazon EventBridge with SNS for scheduling and alerts. Read the case study →
RESULTS
Almost 90%less manual effort
✓ Eliminated Oracle licensing and hardware costs
Asset management company
! PROBLEM
Investor acquisition and agent training data was locked in complex hierarchies inside MongoDB, which standard BI tools and SQL analysts could not query. Reporting was slow and fragmented, on a 24/7 platform that needed zero downtime.
✓ WHAT WE DID
Rapyder built a serverless, real-time data pipeline that converts live MongoDB data into analytics-ready insights, converting JSON to Apache Parquet and delivering business-ready data to a high-performance analytics platform without slowing core systems. Read the case study →
RESULTS
30%faster analytics
✓ Dashboards reflect data in minutes
Up to 30%better query performance from Parquet conversion
Testimonials
Yashas Khoday
Co-founder and CPO of Fyers
Chandra Kholia
CTO of Chalk Digital
Prabhakaran Subramanian
Co-founder and CPO of QBotica Inc
“
We have had a good experience with them so far. We have worked with them for the last 2 years and have never faced any issues.
Yashas Khoday
Co-founder and CPO of Fyers
“
For our every Go Live, Rapyder has been a brilliant partner. Any problem that we faced, the team always came up with quick solutions. Their agile project deployment has been a key element in our success.
Chandra Kholia
CTO of Chalk Digital
“
Very good Services, approachable, friendly and delivers on time! Highly recommend.
Prabhakaran Subramanian
Co-founder and CPO of QBotica Inc
Accelerators
Most data analytics consulting engagements stall at a single dashboard or a one-off pipeline. Rapyder’s don’t, we start from accelerators already built for real engagements.
Rapyder Data Products
Data Foundation & Governance
Insights & Decision Support
A business intelligence solution that implements data extraction, loading and transformation, integrating disparate data sources into a centralized platform for near-real-time analytics and customizable dashboards.
Data shouldn’t be locked behind technical skills. DataDialog AI lets business users ask questions of their data without SQL or BI-tool expertise.
Flags bad data at the source and tracks exactly where every number in a report came from.
Makes every dataset across the business searchable and discoverable, with clear ownership.
Moves on-prem or aging warehouses onto cloud-native platforms without disrupting existing reports.
Lets business teams explore data and build their own views, without filing a ticket with IT.
Surfaces anomalies and trends within minutes of the underlying event, not the next scheduled refresh.
Captures and analyzes device and sensor data at scale, alongside core business data.
Why Rapyder
AWS Premier Tier Services Partner with the AWS Data Services Competency; Microsoft Solutions Partner for Data & AI (Azure); Google Cloud Select Services Partner; Snowflake Premier Partner; Databricks partner.
Pipeline, governance, and dashboard sit under one team, so nothing falls through between vendors.
Data lineage, access controls, and compliance controls designed in from the start.
Rapyder migrates and modernizes legacy data warehouses while keeping existing reports running. This works hand-in-hand with Rapyder’s application modernization services, for teams modernizing the applications that sit on top of that data, and with Rapyder’s cloud migration services, for enterprises still moving off legacy infrastructure entirely.
Rapyder’s engineers sit inside your team, solving problems as they happen.
Rapyder’s data pipelines and dashboards don’t stop working the day they launch, and neither does Rapyder. This is exactly where Rapyder’s managed cloud services take over, keeping pipelines healthy, costs in check, and dashboards accurate long after go-live.
Expert
TALK TO OUR DATA ANALYTICS EXPERT
Head of Data
Gururaja Amaravathi leads Data at Rapyder. With over 16 years designing and delivering enterprise data and cloud platforms, he’s worked across industries like pharma, retail, insurance, banking, and manufacturing, with hands-on depth in AWS, Azure Databricks, Snowflake, Apache Spark, and modern lakehouse architecture. He’s built data lakes, real-time processing pipelines, and enterprise data warehouses that hold up under real production load, and he’s just as comfortable setting architecture strategy as he is talking through a specific data problem.
Have a question about your data analytics journey? Reach out directly.
48-hour response
FAQs
Rapyder is an AWS Premier Tier Services Partner with the AWS Data Services Competency, a Microsoft Solutions Partner for Data & AI (Azure), a Google Cloud Select Services Partner, a Snowflake Premier Partner and a Databricks partner. You don’t have to choose a single platform: Rapyder recommends whichever platform fits your data, budget, and existing technology stack.
It depends on your workloads and existing stack. Snowflake suits SQL-first teams that want a fully managed data warehouse; Databricks suits teams combining data engineering, streaming and machine learning on a lakehouse; Amazon Redshift or Google BigQuery fit teams standardised on one cloud. Rapyder works across all of them and recommends based on your data, skills and budget.
Usually not. Rapyder can modernize your existing data warehouse or integrate new data analytics solutions with what you already have. A full replacement is rarely necessary.
A BI analyst builds reports from existing data. Rapyder’s data analytics services build the underlying pipelines and governance that make sure the data feeding those reports is accurate, timely, and scalable in the first place.
Data engineering builds and runs the pipelines, warehouses and lakehouses that collect, clean and store data. Data analytics uses that prepared data to answer business questions through dashboards, reports, forecasts and AI models. Analytics is only as reliable as the engineering underneath it, which is why Rapyder delivers both as one service.
Yes. Rapyder documents every pipeline and dashboard clearly enough for your team to maintain it independently. If you’d rather not manage it in-house, Rapyder’s Managed Services can keep it running instead.
Data analytics services turn regulatory principles into concrete controls: role-based access for sensitive data, encryption in transit and at rest, masking or tokenization, retention and deletion policies, and lineage from source to report. Rapyder embeds these into the platform, while your legal and compliance teams define the specific obligations.
Rapyder aims to demonstrate a working pipeline or dashboard within weeks, so you can evaluate real value before committing to a larger rollout.
Cost depends on the number of data sources, data volume and refresh frequency, the platform chosen, the number of dashboards and users, and governance or compliance needs. Platform consumption (Snowflake credits, Databricks units or cloud warehouse compute) is billed by the provider separately from implementation. Rapyder scopes and estimates each project after a free Data Assessment.
Rapyder Cloud Solutions, founded in 2017 and headquartered in Bengaluru, is an AWS Premier Tier Services Partner and a Microsoft Solutions Partner with three Azure specializations. It is ISO/IEC 27001:2022 certified, with 400+ employees and 1,000+ projects delivered.
Blogs
May 11, 2024 · 3 min read
Cloud analytics meaning – Cloud analytics is the process of storing and analyzing data in the cloud and using it to extract actionable business…
Aug 12, 2025 · 3 min read
“Data is the new oil, but machine learning is the refinery that turns it into fuel for growth.” – Data Scientist, Tech Innovate Summit…
Mar 18, 2026 · 7 min read
Data is growing faster than most organizations can process it. Every customer interaction, transaction, IoT device, application log, and digital workflow generates data. By…
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