Updated October 2026 · Written and maintained by the Progression Agency strategy team
Big data software development services design and build the systems that collect, move, store, check and serve a company’s data at a scale or speed one application database cannot handle: pipelines, warehouses, lakes, streaming jobs and the analytics and product features that sit on top. They are for product teams, operations and finance leaders, and growing startups whose event data has outrun spreadsheets and nightly exports, and they produce numbers people can trust and query. Progression Agency is based in New York City and works with clients across the United States and worldwide.
On this page · 16 sections
- What are big data software development services?
- When does data become big data, and do you need it?
- Data engineering services: the work underneath every report
- Data pipeline development services: batch and streaming
- Big data vs data warehouse vs data lake: which storage layer?
- Managed cloud services or an open-source stack?
- Big data application development: products that run on your data
- Big data analytics services: reporting people believe
- How do you keep bad data out of the reports?
- Security and privacy on a data platform
- How long does it take to build a data pipeline or a platform?
- How much does big data development cost?
- Engagement models: a project, a team, or hire big data developers
- How buyers ask AI assistants for a big data development company
- How to choose among big data service providers
- Related software, data and AI services
The short answerWe start by testing whether you need a big data platform or a well-modeled warehouse, because many mid-size companies need the second. Then we build ingestion, scheduled pipelines with quality checks, the storage layer and the dashboards, APIs or model features people use. Success is measured in freshness, failed-check rates, query cost and whether two reports agree. A first production slice usually takes two to four months; every project is quoted after a written scope.
Product descriptions come from each vendor’s or project’s own documentation and privacy rules from the regulator or the regulation text, as read in October 2026. Price figures are published US price bands, not a Progression rate card; every build is quoted after a written scope.
What are big data software development services?
They are custom engineering for data that is too large, too fast or too varied for one database and a few scripts. The deliverable is a working platform, written down and owned by you, not a tool subscription.
What big data services usually cover
Six jobs repeat on almost every project: getting data in from each source, moving it on a schedule or as a stream, storing it in a shape that suits analysis, checking it before anyone relies on it, serving it to dashboards and applications, and feeding the models that forecast or score. A good provider is clear about which of these you need now and which can wait.
What do big data developers do day to day?
They write and test the code that connects sources, transforms records and loads tables; they design the data models; they set up the schedules, alerts and access rules; and they spend a surprising share of their time finding out why a number changed. The work sits between backend engineering and analytics, which is why we staff it from both.
Where this page fits next to our other software work
General product builds live on our custom software development page, and Python-specific pipeline work, including how we orchestrate batch jobs and stop bad data, is described on the Python development company page. This page covers the platform as a whole: architecture choices, storage, streaming, quality, ownership, cost and how to choose a partner for big data development services.
When does data become big data, and do you need it?
Data becomes big when the size, speed or mix of it breaks the tools you have, not at a fixed number of gigabytes. Many mid-size companies are better served by a modeled warehouse and reliable pipelines than by a cluster.
| Signal | A warehouse and good pipelines are enough when | A larger platform is worth scoping when |
|---|---|---|
| Volume | Tables are in the millions of rows and queries return in seconds | Raw events run into billions of rows and scans are slow or costly |
| Velocity | Hourly or daily freshness is acceptable to every user | Decisions or product features need data within seconds or minutes |
| Variety | Most sources are databases and SaaS tools with tidy tables | Logs, JSON, documents, images or sensor data must be analyzed together |
| Latency of questions | The same reports run every week | Analysts ask new questions of years of raw history |
| Users | One team reads the reports | Many teams and customer-facing features read the same data |
How much data can be considered big data?
There is no official threshold. A practical test is whether your current database or reporting tool keeps up with the data you already have and the data you will have in two years. If queries still return quickly and storage is affordable, you have a data modeling problem, not a big data problem.
Is big data a good fit for a mid-size company?
Sometimes. A mid-size retailer with clickstream data, a logistics firm tracking vehicles or a software company logging every product event can justify it. A firm whose questions are about sales, finance and marketing totals usually gets more from a managed warehouse, a few well-built pipelines and clear metric definitions.
Data engineering services: the work underneath every report
Data engineering is the discipline of building and running the pipelines and storage that analytics depends on. It is the modern name for most of what buyers mean by big data engineering services, and it is where most project hours go.
What a data engineering company delivers
Source connectors, transformation code under version control, a documented data model, schedules and alerts, quality checks, access rules, a runbook for failures and a handover your own team can operate. Tool vendors sell the components; an engineering partner assembles them around your sources and questions and stays accountable for the result.
ETL or ELT?
Extract, transform, load cleans data before it lands in storage; extract, load, transform lands it raw and reshapes it inside the warehouse. Modern cloud warehouses make the second pattern common because storage is cheap and the raw copy can be replayed. We still transform before loading when data must be filtered for privacy or volume first.
Data engineering consulting before a build
Some companies want an architecture review rather than a build: an outside read of the current pipelines, costs and failure points with a prioritized plan. That engagement is run through our software consulting services practice and often becomes the scope for the build.
Data pipeline development services: batch and streaming
A pipeline moves data from where it is created to where it is used, on a schedule or continuously. Choosing between batch and streaming is the first architectural decision, and the honest answer is usually batch first.
| Question | Batch fits when | Streaming fits when |
|---|---|---|
| How fresh must the data be? | Hours or a day is acceptable | Seconds or minutes change the outcome |
| What triggers the work? | A clock: nightly, hourly, after month end | An event: a click, a payment, a sensor reading |
| How hard is a rerun? | Easy: reprocess a day or a partition | Harder: state, ordering and late events must be handled |
| What does it cost to run? | Compute runs only while jobs run | Compute runs all the time |
| Typical examples | Finance close, marketing reporting, CRM syncs | Fraud flags, live inventory, device alerts, personalization |
How are data pipelines created?
From the question backward: which report or feature needs which fields, from which sources, how fresh. Then each step is written as code with a test, scheduled, given checks on what it receives, and wired to alert a person when it fails. Our batch approach, orchestration and validation rules are set out on the Python pipelines page.
Why are data pipelines important?
Because every dashboard, forecast and data feature is only as good as the pipeline that fed it. A pipeline that silently skips a source or loads duplicates produces confident, wrong numbers, and those are more expensive than no numbers at all.
Real time data processing services
Streaming tools are built for unbounded data. Apache Kafka describes itself as an open-source distributed event streaming platform used for data pipelines, streaming analytics and integration, and Apache Flink as a distributed processing engine for stateful computations over unbounded and bounded streams. On AWS, Kinesis Data Streams collects and processes large streams of records in real time. We choose among them by latency need, the cloud you run and who will operate it.
Who builds data pipelines?
Data engineers, backend engineers with data experience, or analytics engineers for the transformation layer. In a small company the same person often does all three; in our projects the roles are split so one person reviews another’s work.
Big data vs data warehouse vs data lake: which storage layer?
A warehouse stores modeled tables for analysis, a lake stores raw files of any kind cheaply, and a lakehouse adds warehouse behavior on top of lake storage. Pick by the data you hold and the questions you ask, not by fashion.
| Layer | What it holds | Strength | Watch for | Fits when |
|---|---|---|---|---|
| Data warehouse | Modeled, structured tables | Fast SQL, clear definitions, easy access control | Raw and unstructured data fit poorly | Most reporting and finance use cases |
| Data lake | Raw files in open formats on object storage | Cheap, keeps everything, any data type | Becomes a swamp without catalog and rules | Large raw history, logs, files, model training |
| Lakehouse | Lake files with table formats on top | One copy serves SQL and data science | More moving parts to run | Both analytics and machine learning on large raw data |
| Operational database | Current application records | Transactions and consistency | Heavy analytics slows the app | Small reporting needs, read replicas |
Data warehouse development services
Warehouse work is mostly modeling: deciding what a customer, an order and a session mean, building tables named for the business, and documenting each metric. Managed warehouses take care of the servers. Snowflake’s documentation says it separates storage and compute and runs queries in virtual warehouses, which are independent compute clusters; Google describes BigQuery as a fully managed data platform whose compute and storage layers operate independently.
Data lake development
A lake is cheap to fill and easy to ruin. The build includes a folder and naming convention, a catalog of what each dataset is, retention rules, and access controls by dataset. Without those, nobody can find or trust what is stored.
What a lakehouse adds
Databricks defines a data lakehouse as a data management system combining the benefits of lakes and warehouses. Open table formats make that possible: Apache Iceberg calls itself a high-performance format for huge analytic tables and lists engines such as Spark, Trino, Flink and Presto that can work with the same tables.
Not sure you need a big data platform at all?Send us your data sources, rough volumes and the questions your team cannot answer today. We will tell you whether a well-built warehouse covers it, before you spend on anything larger.
Managed cloud services or an open-source stack?
Managed services trade some control and a usage bill for far less operations work; open-source engines trade the reverse. Most of our recommendations start managed and add open-source components only where a workload needs them.
| Name | What its documentation says it is | Consider it when |
|---|---|---|
| Apache Spark | A multi-language engine for data engineering, data science and machine learning on single machines or clusters | Large batch transformations or model training outgrow SQL alone |
| Apache Kafka | An open-source distributed event streaming platform | Many systems must publish and read the same event streams |
| Apache Flink | A distributed engine for stateful computations over unbounded and bounded streams | Streaming logic needs state, windows or exact ordering |
| AWS Glue | A serverless data integration service to discover, prepare, move and integrate data | You run on AWS and want managed ETL and a data catalog |
| Amazon Kinesis Data Streams | A service to collect and process large streams of data records in real time | You run on AWS and need a managed stream |
| Google BigQuery | A fully managed, AI-ready data platform with independent compute and storage | You run on Google Cloud or want a serverless warehouse |
| Snowflake | A platform that separates storage and compute, with independent virtual warehouses | Several teams need isolated compute on shared data |
| Apache Iceberg | A high-performance table format for huge analytic tables | Several engines must share one copy of lake data |
Big data services in AWS
On AWS the common building blocks are object storage for the lake, AWS Glue for serverless integration and cataloging, Kinesis for streams, and a warehouse or query engine on top. Glue’s documentation describes ETL, ELT and streaming support in one service and more than 70 data sources. Azure and Google Cloud offer equivalents; our cloud app development page covers how we choose between the three clouds.
Do we need Hadoop or Spark?
Decide by workload. Apache Spark unifies batch and real-time stream processing in Python, SQL, Scala, Java or R and scales from a laptop to a cluster. Many companies that once ran on-premises clusters now run the same jobs on managed services or inside a warehouse, and the right answer for a new build is often neither until the data demands it.
Big data as a service versus a custom build
Managed platforms sell storage, compute and tooling by usage, so there is no cluster to run. You still need someone to design the model, write the pipelines, set the checks and own the definitions. That is the custom part, and it is what we build. The general trade-off is weighed on our custom versus off-the-shelf software guide.
Big data application development: products that run on your data
Some platforms exist to serve reports; others exist to power features inside a product. Big data application development services build the second kind: applications whose core value depends on large or fast data.
How can big data be utilized within a software system?
As a feature customers see (usage analytics, live tracking, recommendations), as a decision a system makes automatically (fraud flags, pricing rules, routing), or as a search or question-answering layer over large records. Each needs an API or serving layer between the platform and the app, which our API development and backend development teams build.
Customer-facing analytics
When customers see their own data inside your product, the platform needs tenant-level access rules, predictable query speed and costs that scale with usage. Embedded reporting is designed and priced on our dashboard development page.
Machine learning and AI features
Forecasts, scoring and classification run on the same cleaned tables the reports use, which is one reason to build the platform well first. Model work in Python is described on the Python page, and adding language models over governed data is covered by our AI integration services.
Device and logistics data
Sensor readings and vehicle positions are classic high-velocity sources. Device-side work sits on our IoT app development page and fleet and warehouse systems on logistics software development; this page covers the platform that receives their data.
Big data analytics services: reporting people believe
Analytics on a big data platform fails for the same reason it fails anywhere: two reports define the same number differently. Our analytics work starts with definitions and owners, then builds dashboards on modeled tables.
Metric definitions before charts
Each metric gets a written definition, a formula, an owner and the table it comes from. When finance and sales disagree about revenue, the disagreement is settled once in the model, not in every dashboard.
Dashboards and self-service
We build in the business intelligence tool you already license or in custom charts inside your product. Marketing attribution and campaign tracking are a separate specialty handled by our marketing analytics agency team.
Data analytics software development
Sometimes the right answer is a small internal application rather than another dashboard: a planning tool that writes back to the warehouse, an exception queue for operations, or an export service for partners. Those are scoped as software, with users, roles and tests.
How do you keep bad data out of the reports?
With checks at every step that stop the run when data looks wrong, and a person alerted to fix it. A platform that loads questionable data quietly is worse than one that pauses.
- Row counts compared with the source and with the previous run
- Required fields present and keys unique
- Values inside expected ranges and known categories
- Freshness: the newest record is as recent as it should be
- Schema changes at the source caught before they break a table
- Reconciliation of key totals against the system of record
- A named owner for every dataset and every alert
Lineage you can follow
Every table records which sources and steps produced it, so a strange number can be traced back in minutes. Lineage also tells you what breaks downstream before you change a source.
Who owns the data and the code after the project?
You do. The code lives in your repository, the cloud accounts are in your name, the documentation and runbooks are handed over, and nothing depends on a license of ours. If you later bring the work in-house, your team inherits a working system.
Security and privacy on a data platform
A platform that gathers everything is also the most attractive target and the place privacy rules bite hardest. We design access, retention and masking in from the first table, inside the rules that apply to your data.
Least privilege and masking
Access is granted by role and dataset, sensitive columns are masked or tokenized for most users, and service accounts get only what their job needs. Access logs are kept so you can answer who saw what.
GDPR principles that shape the design
For personal data of people in the EU, Article 5 of the GDPR requires collection for specified purposes, data limited to what is necessary for those purposes, and storage in identifiable form no longer than necessary. On a platform those become purpose tags, column-level minimization and retention jobs that actually delete.
California privacy rights
California’s attorney general lists the consumer rights under the California Consumer Privacy Act: to know, delete, opt out of sale or sharing, correct and limit use of sensitive personal information. It applies to for-profit businesses meeting thresholds such as gross annual revenue over $25 million. A platform should be able to find and act on one person’s records across every table.
Health data
Protected health information brings its own safeguards and contracts. Those are covered on our HIPAA-compliant software development page. We build inside these rules; your counsel decides how they apply to you.
How long does it take to build a data pipeline or a platform?
A single well-scoped pipeline can be in production in a few weeks; a first platform release with several sources, modeling and dashboards usually takes two to four months, and a broad platform longer.
| Phase | What is delivered | How progress is measured |
|---|---|---|
| Discovery | Source inventory, the questions to answer, owners, access plan | Signed-off list of priority questions and sources |
| Architecture and scope | Storage, pipeline and serving choices with reasons; a written scope | Scope accepted; costs estimated for running |
| Thin vertical slice | One source through to one trusted report or API | Numbers reconcile with the system of record |
| Expansion | More sources, a modeled layer, quality checks on each step | Freshness targets met; failed-check rate falling |
| Serving and access | Dashboards, APIs, roles and masking | Users served; access reviewed |
| Operation | Monitoring, cost tuning, documentation, handover | Incidents, query cost and time to answer new questions |
Why a thin slice comes first
Taking one source all the way to one report proves the access, the tools, the model and the checks before money is spent on twenty sources. It also gives the business something useful in weeks rather than at the end.
What stretches a timeline
Slow access approvals, undocumented source systems, disputed metric definitions and late privacy reviews. Each is cheaper to settle in discovery than mid-build.
Reports that disagree with each other?We trace two conflicting numbers back to their sources and show where the definitions or the pipelines split, then scope the fix.
How much does big data development cost?
Big data software development services are quoted after a written scope, because source count, volume and latency drive the figure. The bands below are published US price bands (market figures, not a Progression rate card) already on this site for comparable software work.
| Engagement | Price band | Typical timeline | Where the band is published |
|---|---|---|---|
| Technical assessment or code audit | $5,000 to $15,000 | 2 to 4 weeks | Software consulting |
| Architecture and roadmap | $10,000 to $40,000 | 3 to 8 weeks | Software consulting |
| Integration or automation | $15,000 to $50,000 | 4 to 10 weeks | Custom software |
| Focused BI dashboard | $10,000 to $40,000 | 2 to 6 weeks | Dashboard development |
| Multi-source reporting suite | $40,000 to $100,000 | 2 to 4 months | Dashboard development |
| Internal tool or dashboard | $25,000 to $75,000 | 2 to 4 months | Custom software |
| Business platform | $75,000 to $250,000 | 4 to 8 months | Custom software |
| Enterprise system or modernization | $250,000+ | 6 to 12+ months | Custom software |
What moves the price
The number of sources and how clean they are, the freshness required, the volume and its growth, the number of user roles and access rules, compliance needs, and whether the platform feeds customer-facing features. Streaming adds build and running cost.
Running costs are separate
Cloud storage, compute and tool licenses are billed by the providers, mostly by usage. Part of every scope is an estimate of monthly running cost and the design choices, such as partitioning and scheduled rather than continuous compute, that keep it down.
Engagement models: a project, a team, or hire big data developers
There are three common ways to work with us: a fixed-scope project, an ongoing team that builds and runs the platform, or individual engineers added to your own team.
| Model | How it works | Best when |
|---|---|---|
| Scoped project | Written scope, milestones, acceptance tests, handover | The first platform release or a defined migration |
| Ongoing team | A small team builds new sources and features and runs the platform | Data needs keep growing and you have no data team yet |
| Dedicated engineers | Engineers join your team and work in your tools and rituals | You have a data lead and need hands |
Hire big data developers through a dedicated team
If you already have a data lead and need engineers, the terms, notice and monthly price bands are on our hire dedicated developers page. The code and accounts stay yours either way.
Larger programs
When a platform is one part of a wider modernization, it is run alongside our enterprise application development work so the data model and the applications change together.
How buyers ask AI assistants for a big data development company
Data leaders increasingly ask ChatGPT, Claude, Perplexity, Gemini, Microsoft Copilot and Google AI Overviews to shortlist providers or explain an architecture choice before they talk to anyone.
The prompts
Typical questions: who builds data pipelines for a mid-size company, which data engineering company works with our cloud, do we need a lakehouse or a warehouse, and what a first data platform costs. The answers are assembled from pages that state scope, prices, timelines and trade-offs plainly.
What gets cited
Vendor documentation for product facts, comparison tables, provider pages with clear service lists and published price bands, review sites and directories, and technical articles that explain a decision. Pages that hedge every claim or hide prices are harder for an assistant to quote.
What a data team should publish to be named
If you build data products yourself, the same rules apply: plain answers to the questions your buyers ask, specific numbers you can stand behind, and consistent company facts across your site and profiles. Our AI consulting team can advise on where language models fit in your own product.
How to choose among big data service providers
For big data software development services, choose the provider that asks about your questions before your tools, shows how it tests and hands over, and puts its assumptions in writing. The requirements below can each be checked before you sign.
| Requirement | How to check it |
|---|---|
| Starts from business questions | Ask what they would want to know in week one; tools should come later |
| Will tell you when a warehouse is enough | Ask when they last recommended a smaller build |
| Quality checks are part of the scope | Ask to see the checks listed in a proposal |
| You own code, accounts and documentation | Read the contract’s IP and handover clauses |
| Running costs estimated up front | Ask for a monthly cost estimate with assumptions |
| Privacy designed in | Ask how retention and deletion requests are handled across tables |
| A thin slice before scale | Ask what the first production deliverable is and when |
- Ask which sources they would load first and why
- Ask how a failed check is reported and who fixes it
- Ask how metric definitions are written and approved
- Ask how they would cut the running bill in half if needed
- Ask what documentation your team receives at handover
Red flags
A tool chosen before any discovery, a promise of real-time everything, no mention of data quality, prices with no written scope, or a platform that only the vendor can run.
Related software, data and AI services
These pages cover the work that connects to a data platform, from the applications that create data to the dashboards and models that use it.
- Custom software development services and published price bands
- Python back ends, batch pipelines and automation
- Business dashboards and embedded reporting
- Cloud-native applications on AWS, Azure and Google Cloud
- Adding AI features to existing systems
- AI strategy, readiness and governance
- Architecture reviews and build versus buy advice
- APIs that serve data to apps and partners
- Backend engineering, databases and scaling
- Enterprise applications and modernization
- Dedicated engineers for your own team
- Custom software versus off-the-shelf tools
- Marketing analytics, attribution and tracking
- Software for protected health data
- Connected device applications
- Logistics and fleet software
- SaaS product development
- Web and mobile app development
Get a data platform scoped around your questions
Tell us your sources, rough volumes, the freshness you need and the questions nobody can answer today. We will send a free proposal for big data software development services with the architecture we would start with, the first slice and what it would take to run.
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Frequently asked questions
What are big data services, and which ones does a growing company actually need?
What is big data engineering?
What does data pipeline development involve, from source system to warehouse?
What is big data as a service, and does it replace custom engineering?
What big data solutions are typically required, and for what?
Do mid-size companies need big data software development services?
How many months does a first data platform release usually take?
How quickly can one data pipeline reach production?
How much does a big data project cost?
What drives big data development cost up?
Should our pipelines be batch or streaming?
Do we need Hadoop or Spark?
What is the difference between a data warehouse and a data lake?
How do you keep bad data out of reports?
Who owns the data and code after the project?
How do you handle personal data on a data platform?
Can you build data features into our product, not just reports?
Before kickoff, what should we prepare for a big data development company?
Can you improve the warehouse and pipelines we already run instead of replacing them?
Which cloud do you build data platforms on?
Do you offer data engineering services as an ongoing team?
Do you build data platforms for companies in other states and countries?
Not sure you need a big data platform at all?Send us your data sources, rough volumes and the questions your team cannot answer today. We will tell you whether a well-built warehouse covers it, before you spend on anything larger.
Get a free marketing proposal
Tell us what you are trying to grow and we will come back with a plan, not a pitch deck. Same-day reply on weekdays.