Data Volume & Workload Assessment
We profile your data growth, query patterns and latency needs, so the platform is sized for real load, not guesses.
- Workload analysis
- Capacity plan
Data & Analytics
Big Data Development Services
We engineer distributed processing on Spark, Kafka and data lake storage, tuned for throughput and cost, so large batch and event workloads finish reliably as volumes grow.
- Spark processing
- Kafka streaming
- Data lake storage
- Performance tuning
- Years of delivery
- 8+
- Years of delivery
- Projects shipped
- 350+
- Projects shipped
- Clients across US, UK, AU & SG
- 200+
- Clients across US, UK, AU & SG
What’s included
Everything You Need to Process Big Data at Scale
We architect and build Spark, Kafka and distributed storage platforms that handle terabytes without slowing your business down.
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Distributed Data Lake Architecture
We design Hadoop-compatible and cloud object storage layers with partitioning and tiering that keep storage costs in check.
- Lake architecture
- Storage strategy
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Apache Spark Processing Jobs
We build and tune Spark applications for batch transformation and analytics, cutting multi-hour jobs to runs your team can rely on.
- Spark applications
- Performance benchmarks
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Kafka Event Streaming Pipelines
We implement Kafka topics, producers and consumers that capture events from apps and devices as they happen.
- Kafka cluster setup
- Stream consumers
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NoSQL & Warehouse Integration
Processed data lands in Cassandra, MongoDB or your warehouse, so analysts and applications query it without extra work.
- Sink connectors
- Schema mappings
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Cluster Security & Data Access Governance
We apply encryption, authentication and audit logging across the cluster to protect sensitive records at scale.
- Security configuration
- Audit logging
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Cloud Cluster Deployment & Autoscaling
We deploy on EMR, Databricks or Kubernetes with autoscaling, so capacity follows demand and idle spend drops.
- IaC templates
- Autoscaling policies
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Cluster Monitoring & Performance Tuning
We watch job health, resolve bottlenecks and tune resources every month so the platform stays fast as data grows.
- Monitoring dashboards
- SLA-based support
Need something specific?
Why Kramate
Why Business Owners Choose Kramate for Big Data Projects
We size things to what you really have, and we do not sell you a bigger system than you need.
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Sized for the Data You Really Have
Instead of
paying for a giant system you never fill
With Kramate
We measure your actual data and growth first, then size the plan to fit.
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Costs That Shrink When Work Slows
Instead of: paying full price for machines sitting idle
With Kramate: You only pay for the power you use, and we show you the monthly bill.
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Big Ideas in Small Words
Instead of: meetings full of terms you cannot follow
With Kramate: We explain what each part does for your business, and we leave the jargon out.
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Private Records Stay Private
Instead of: worrying who can open customer information
With Kramate: We lock access down from the first day, and a second person checks all the work.
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Help Whenever the System Needs It
Instead of: a system with nobody watching it
With Kramate: We watch it for you, and our support plans include around-the-clock cover where you need it.
Our approach
How We Run Your Big Data Development, Step by Step
We make very large data sets fast to store, search and study.
7 clear steps 7 deliverables you keep
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Size & Speed Check
We measure how much data you create each day, where it comes from and what you do with it.
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Data Lake Design
We plan a large shared store where raw data lands first, then gets sorted for use.
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Spark Job Building
We build jobs, often on Spark, that crunch huge files in parallel so answers arrive in minutes, not days.
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Connect Your Systems
We join the new store to your databases, live event feeds and reporting tools.
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Lock Down Access
We set rules for who may read or change each data set, and record who did.
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Launch & Autoscale
We switch on the cluster in the cloud so it grows when work is heavy and shrinks when quiet.
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Watch & Tune
We track job times and costs, and speed up the slowest tasks as your data grows.
Tools & technologies
Technologies We Use for Big Data Development
We choose distributed tools sized to your data volume, latency needs and cloud budget.
- Apache Spark: Processes very large datasets quickly across many machines.
- Apache Flink: Processes live event streams with low delay and accurate results.
- Apache Hadoop: Distributed storage and processing for existing large data clusters.
- Apache Hive: Runs SQL queries over large datasets stored in a data lake.
- Databricks: Unified platform for big data processing, analytics and machine learning.
- Apache Kafka: Streams events between systems in real time.
- Apache NiFi: Visual tool for routing and transforming data flows between systems.
- Apache Pulsar: Messaging platform for event streaming across many applications.
- Apache Cassandra: Distributed database for high-volume writes across many servers.
- MongoDB: Flexible document database for fast-changing data.
- Apache HBase: Wide-column store for fast access to very large tables on Hadoop.
- Apache Parquet: Compact columnar file format that keeps large datasets cheap to query.
- MinIO: Object storage that gives a data lake a home on your own servers.
- Apache Airflow: Schedules and monitors data and model pipelines.
- Kubernetes: Runs and scales containerised software automatically.
- Terraform: Defines cloud setups as code so they can be repeated.
- Prometheus: Collects metrics to watch systems and model health.
- Grafana: Dashboards and alerts for live system metrics.
Expert guide
Big Data Development Services: A Complete Guide for Businesses
Big data development services design and build systems that store and process data too large, too fast or too varied for a single database server. They typically combine distributed storage, batch and stream processing engines, and open table formats, running on cloud or on-premises clusters.
The honest first question is whether you need any of that. Many businesses with impressive-sounding datasets are served better by a well-tuned warehouse, and distributed systems add operating cost, new skills to hire and more places for failures to hide.
This guide gives a practical scale test, explains what Spark, Kafka, Flink and Iceberg each do, discusses architecture patterns, security, regional rules in the USA, UK, Australia and Singapore, and the levers that keep cluster costs under control.
The scale test: do you need big data technology?
The usual description of big data is volume, velocity and variety. The NIST Big Data Interoperability Framework (opens in a new tab) uses these characteristics to define what makes a system a big data system. Use them as a checklist rather than a label.
- Volume: do queries on a single well-indexed warehouse already run slowly after tuning?
- Velocity: must events be processed continuously as they arrive, at rates one machine cannot handle?
- Variety: are you mixing logs, images, documents and sensor readings that do not fit tables?
- Retention: must you keep raw history cheaply for years, such as for audits or model training?
If the answer is no across the board, consider a cloud data warehouse first. If you answer yes to one or two, you probably need distributed processing for that workload only.
What big data development services cover
Big data development services span the whole path from raw events to usable results: ingesting data from applications, devices and files, storing it cheaply at scale, processing it in batch or as streams, exposing curated tables to analysts, and operating the platform with monitoring and cost controls.
It also includes the unglamorous parts that decide success: file layout and partitioning, schema management, retention policies, access control and a plan for replaying data after a bug.
What each major technology actually does
Open source tools in this space are building blocks. Each does one job well, and most architectures combine several.
| Technology | Primary job | Use it when |
|---|---|---|
| Apache Spark (opens in a new tab) | Distributed batch processing, SQL and machine learning on large datasets | Transforming or training on data that exceeds one machine |
| Apache Kafka (opens in a new tab) | A durable log that carries event streams between systems | Many services need the same events, with replay ability |
| Apache Flink (opens in a new tab) | Stateful stream processing with event-time handling | Continuous computation such as windows, joins and alerts |
| Apache Iceberg (opens in a new tab) | An open table format for large analytic tables on object storage | You want warehouse-like tables, schema changes and time travel on a lake |
Managed platforms such as Databricks (opens in a new tab) bundle several of these. They reduce operations work but tie costs and features to a vendor, so compare them with self-managed options.
Architecture patterns: lake, warehouse and lakehouse
A data lake stores raw files in cheap object storage and offers flexibility at the price of governance work. A warehouse offers structured, fast, governed tables. A lakehouse applies table formats such as Iceberg so one copy of data serves both purposes.
A common design lands raw data first, refines it through cleaned and business-ready layers, and keeps each layer reproducible from the one before it. Streaming and batch paths should share business logic where possible, to avoid two sets of numbers. See real-time analytics solutions for the streaming side.
Security and governance at scale
Large platforms collect wide, long-lived copies of data, which raises the stakes. Encrypt storage and network traffic, give each job and person only the access needed, separate production from experiments, and log access to sensitive datasets.
Catalog what you hold and why (a master data management effort can help with core entities), set retention periods, and make deletion workable: open table formats can support row-level deletes, but you must plan for them. For encryption and key practices, see data security and encryption services.
Efficient compute and sustainability
Idle clusters waste money and energy. Favor autoscaling, shut down interactive clusters when unused, store data in compressed columnar formats, process only new or changed data, and retire datasets nobody uses.
The cheapest computation is the one you avoid: sampling for exploration, pruning partitions and caching repeated results all reduce work.
Industry use cases
- Telecommunications: network event streams processed continuously to spot faults and congestion.
- Manufacturing: sensor readings stored for years and used to train maintenance models.
- Advertising technology: impression and click logs aggregated for reporting and bidding analysis.
- Energy: smart meter readings loaded in bulk and compared against forecasts.
These are illustrative examples, not Kramate client results.
Regional considerations: where data lives and flows
Big platforms often replicate data across regions for resilience, which can conflict with residency and transfer rules. Confirm with legal counsel.
- UK: UK GDPR rules on international transfers are explained in the ICO’s guidance (opens in a new tab).
- Singapore: the PDPC’s Personal Data Protection Act resources (opens in a new tab) describe transfer and protection obligations.
- Australia: Australian Privacy Principle 8 (opens in a new tab) concerns disclosure of personal information overseas.
- USA: state laws such as the CCPA (opens in a new tab) and sector rules like HIPAA apply according to the data held.
Pick cloud regions deliberately, document replication paths, and ask whether support staff can view production data from another country.
Cost factors and budgeting
Two budgets exist: building the platform and running it. Running cost often surprises teams because it scales with usage.
- Data volume stored, and how long it is retained.
- Compute hours for batch jobs and for always-on streaming.
- Number of data sources, formats and consumers.
- Latency targets, since lower latency means always-on resources.
- Managed platform fees compared with the staff time to self-manage.
- Governance, monitoring and support requirements.
Set a cost alert per workload, tag resources by team and review spend monthly. Begin with one workload that justifies the platform and prove its economics before migrating others.
Choosing among big data development services
- Do they challenge the need for distributed systems before proposing them?
- Can they explain file layout, partitioning and skew handling?
- Do they design for cost, with autoscaling and spend alerts?
- Is the stack open, so you can move workloads later?
- Will they leave runbooks and train your operators?
How Kramate delivers big data work
Discussions start under an NDA. After a free consultation we return a detailed estimate within 24 hours, offered as fixed-price, dedicated-team or hourly. Your dedicated project manager posts daily on Slack or Teams, and every two weeks we demonstrate progress. You receive all intellectual property, and our engineers can lead or join your team. Read about our approach.
Frequently asked questions
Do I need big data technology?
Only if one well-tuned warehouse or database cannot handle your volume, event rate or variety of data. Measure first. Many organizations are better served by a cloud warehouse and add distributed tools for a specific workload.
What is the difference between Spark and Flink?
Spark excels at large batch processing, SQL and machine learning, and also supports streaming. Flink is designed around continuous, stateful stream processing with careful handling of event time. Many teams use Spark for batch and Flink for streams.
Data lake, data warehouse or lakehouse?
A lake stores raw files cheaply, a warehouse offers governed, fast structured tables, and a lakehouse adds table formats so one copy of data serves both. Your mix of data types and users should drive the choice.
What is Apache Iceberg?
Iceberg is an open table format that organizes files in object storage into tables with schema evolution, snapshots and time travel. Multiple engines can read the same tables, which helps avoid lock-in.
How can we reduce big data cloud costs?
Turn off idle clusters, autoscale, store compressed columnar files, process only changed data, fix small-file and skew problems, tag spending by team and review it monthly. Remove datasets nobody uses.
What are the five Vs of big data?
They are volume, velocity, variety, veracity and value. The first three describe size, speed and diversity, while veracity concerns trustworthiness and value concerns whether the data answers a worthwhile question for the business.
Sources and further reading
- NIST Big Data Interoperability Framework (opens in a new tab)NIST
- Apache Spark Documentation (opens in a new tab)Apache Software Foundation
- Apache Kafka Documentation (opens in a new tab)Apache Software Foundation
- Apache Flink Documentation (opens in a new tab)Apache Software Foundation
- Apache Iceberg Documentation (opens in a new tab)Apache Software Foundation
- Databricks Documentation (opens in a new tab)Databricks
- UK GDPR guidance and resources (opens in a new tab)UK ICO
- Personal Data Protection Commission Singapore (opens in a new tab)PDPC
- Australian Privacy Principles (opens in a new tab)OAIC
- California Consumer Privacy Act (CCPA) (opens in a new tab)California Attorney General
FAQ
Frequently asked questions
Data volume, velocity and the number of sources drive the price most. Other factors are the tools involved, such as Spark, Kafka or cloud storage, plus how much processing logic you need. Retention rules, security needs, and how many teams will query the platform also change the effort. We give a no-obligation estimate within 24 hours.
A typical build runs through discovery, architecture, ingestion, processing, testing and handover. Duration depends on how many sources you connect, how messy the data is and how fast your team approves decisions. A first working pipeline often comes earlier than the full platform, so you can see value in stages.
You own them. Kramate signs an NDA before detailed discussions, and the pipelines, job code, schemas and documentation transfer to you at handover. Your data stays in your cloud accounts, so we never hold it. That keeps access, audit trails and ownership clear from day one.
Yes. We build on the cloud account and tools you already use, whether that is AWS, Azure or GCP, and connect to existing databases and BI tools. We agree naming, access and deployment conventions first, so your engineers can read, run and extend what we deliver.
You need them when data no longer fits on one machine, arrives continuously, or takes too long to process in batches. If your data is modest, a standard warehouse is simpler and cheaper to run. We check your real volumes before recommending distributed tools.
Prepare a list of data sources, rough volumes, the questions you want answered and who will use the results. Access to sample data helps a lot. Know your retention and privacy rules too. Even partial answers let us scope the work and spot risks early.
Look at pipeline reliability, data freshness, processing time against your needs, and cost per workload. Check that users can reach trusted results without waiting. We set these measures with you at the start and add monitoring and alerts, so problems are visible before users notice.
Taking new projects
Let’s talk about your Big Data Development Services needs
Tell us about your project and get a free consultation and estimate within 24 hours.
- We reply within 4 business hours A real consultant reads your brief — no bot, no queue.
- 30-minute discovery call We dig into goals, constraints and budget. NDA signed on request.
- Proposal in 24 hours Scope, timeline, team and price — clear enough to decide on.
NDA on request You own the IP No obligation Free estimate