AWS data engineering
Intsurfing AWS services for big data
Amazon data pipeline
Leverage our Amazon data pipeline service to automate data movement across AWS. We design AWS-native pipelines using Glue, Kinesis, and Lambda, so your data moves in real-time or batches, just the way your project needs it. Logging, monitoring, and error handling—all covered.
AWS system architecture
Want to build a high-performance data system on AWS? We design AWS cloud architecture that handles large-scale data processing, storage, and analytics. Our engineers create event-driven and microservices-based architectures using Lambda, API Gateway, and SQS. For cost-effective storage, we structure S3 tiers and optimize Redshift for complex queries.
Data storage
With Intsurfing AWS data storage services, you obtain a scalable, cost-effective storage strategy. We configure S3 with lifecycle policies and Intelligent-Tiering to optimize costs. For high-performance workloads, we implement EBS volumes with IOPS tuning and RAID configurations. Need structured storage? We design DynamoDB and RDS solutions with auto-scaling, read replicas, and multi-AZ deployments for reliability.
System optimization
Struggle with slow queries, high storage costs, or data bottlenecks? With our data engineering AWS services, optimize your existing infrastructure for speed and cost efficiency. We fine-tune Redshift clusters with distribution keys and sort keys, restructure S3 data with optimized partitioning, and implement caching. Need better ETL performance? We refactor Glue jobs, optimize Spark workloads on EMR, and implement parallel processing to cut execution time.
Things our big data Amazon web services help you with
Waiting hours for data to process? Outsource Intsurfing AWS cloud services for big data, and we'll fix it. Slow Spark jobs? We fine-tune executors, memory allocation, and shuffle settings on EMR. Batch processing taking too long? We optimize Glue jobs with pushdown predicates and parallel execution. For real-time data, we configure Kinesis and Kafka to handle high-throughput streams with minimal lag. Your data moves faster, and your team gets insights when they need them.
AWS costs out of control? You're probably paying for resources you don’t need. With our Amazon cloud computing for big data expertise, you only pay for what you actually use. We analyze compute usage, right-size EC2 instances, and switch to spot instances where possible. Storage eating your budget? We implement lifecycle policies in S3, moving infrequently accessed data to Glacier. Redshift running expensive queries? We optimize them, reduce duplication, and restructure your data for faster, cheaper analytics.
Sometimes, the systems get too complex, and you have no control over when your data moves. Our Amazon Web Service big data management services solve this problem. We configure Kinesis and SQS to process messages at a controlled rate, so you face no sudden spikes or delays. Using Step Functions, we orchestrate complex workflows, so every job runs exactly when it should.
Old systems can’t handle modern data workloads. Remove those limits with AWS cloud computing for big data. If your database crashes under heavy queries, we migrate to Redshift with optimized distribution keys. If batch processing is too slow, we implement real-time streaming with Lambda and Kinesis. Limited compute power? We set up auto-scaling clusters with EMR, so resources expand as needed.
A small typo, an extra space, or an unexpected format—your system rejects it. We replace strict string comparisons with flexible search and weighted scoring models, so small typos don’t cause mismatches. For large datasets, we implement machine learning-based anomaly detection, allowing your system to refine results dynamically. No more lost data with our Amazon Web Services big data solutions—just better pattern recognition, adaptive processing, and higher accuracy.
Success stories of our clients
PDF data extraction
Learn how Intsurfing parsed 18M PDFs, identified 1.2M turnover data points, and kept costs as low as 0.7 cents per 1,000 files.
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Data collection tool
Learn how Intsurfing developed a data collection platform that allows developers with basic coding skills to get data from online sources.
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ETL for India voter data
Learn how Intsurfing arranged the ETL pipeline for our client to collect data on Indian voters from local portals. Sample data included.
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Address processing system
Discover how the Intsurfing team made an address parsing system 2x faster with Redis, new verification algorithms, caching, and deduplication.
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Data Pipeline and Search Engine Optimization
Cut processing time in half, speed up search 10x. Learn how we optimized ETL pipelines, storage, and indexing for real-time data insights.
Learn more->Why choose Intsurfing AWS service for big data
Focus on big data
Not all cloud solutions are built for big data. Ours are. Our entire approach to data engineering with AWS is built for handling large-scale data workloads. We design pipelines that process terabytes of data. We optimize storage to keep costs down. Whether it’s ETL, analytics, or cloud storage, we structure everything for speed, scale, and reliability.
Proven expertise
Big data projects fail without the right expertise. That’s why our AWS certified data engineers do more than build pipelines—we optimize every part of your architecture. We analyze your compute needs and configure Kinesis and Kafka to process millions of events per second. All to build systems that process massive datasets, optimize storage, and simplify analytics.
Pragmatic AWS engineering
AWS has a tool for everything, but that doesn’t mean you need them all. We take a pragmatic approach to AWS engineering, cutting out the complexity and focusing on what actually moves your data faster. Running batch jobs that should be real-time? We fix that. Paying for oversized compute instances? We right-size your resources. Our approach is simple: use what works, skip what doesn’t, and make your data move the way it should.
How Intsurfing implements AWS for big data
Outsourcing
Some projects don’t need a long-term team—they just need the right solution. Our outsourcing model is built for that. We take care of the entire process—whether you need a high-performance data pipeline, a custom-built dataset, or a system overhaul. You set the goal, we handle the technical work. No need to assemble a team or oversee daily operations. When we deliver, it’s ready to use and fully optimized.
Managed team
Building a capable data team takes time. With our Managed Team, you skip the hiring process and get a fully operational unit ready to deliver. We handle everything—recruitment, onboarding, and technical management. Want them to integrate with your in-house team? No problem. Prefer them to work independently? We’ve got that covered too.
Make big data work for you
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