Organizations generate massive volumes of data from various sources, including social media, IoT devices, customer interactions, and more. This data, often referred to as “big data,” holds immense potential for insights and innovation. However, traditional data processing and analysis tools struggle to handle the sheer volume, variety, and velocity of big data.
Oblytech’s Big Data & Cloud Analytics services help your organization get more from its data. We use current cloud platforms and tools to process, analyze, and visualize large data sets, finding patterns, trends, and opportunities that matter to the business.
We train your team to use the tools needed to access and analyze data on its own, so reporting does not wait on a single specialist.
We create customized reports and dashboards tailored to your specific KPIs and business requirements, providing you with the insights you need to make informed decisions.
We help you select and implement the right BI tools for your needs, such as Tableau, Power BI, or QlikView, enabling you to create interactive dashboards, reports, and visualizations.
We design data models and schemas that optimize data storage, retrieval, and analysis, ensuring efficient query performance and data integrity.
We create efficient ETL processes to extract data from various sources, transform it into a usable format, and load it into your data warehouse.
We design and build data warehouses that consolidate data from scattered sources, keeping it accurate, consistent, and easy to reach for analysis.
Big data is a term that is often over-requested in analytics procurement. Some organizations asking for it have only a few hundred gigabytes of transactional records, a warehouse that is slow in one report, and a team that has not yet learned to query what it has.
In those cases the answer is usually better modeling, indexing or a more disciplined extract, not a distributed processing platform. The decision this page supports is different: it is for a business whose data volume, arrival speed or source variety has genuinely outgrown a conventional store, and which needs to know what the move would involve.
The useful test is practical. Can the current platform answer the questions the business asks within a time that matters, at a cost that is predictable? If it can, the investment belongs elsewhere. If it cannot, the next question is which of three things is failing: the volume being stored, the speed at which records arrive, or the number of different formats that must be joined.
Each has a different remedy, and cloud platforms are only one of them.
The three properties that define big data are often listed together, but they lead to different designs. A buyer who understands which one is the real constraint avoids paying for capacity they do not need.
| Constraint | Symptom in day-to-day work | Remedy that usually fits first |
|---|---|---|
| Volume | Historical queries slow down year on year and storage costs rise faster than the business | Partitioning, data tiering to cheaper storage and retention rules agreed with the data owners |
| Velocity | Records arrive faster than batch loads can process, and reports lag behind operations | Incremental or event-driven ingestion, with a clear decision about which reports truly need near-real-time data |
| Variety | Analysts often spend much of their time reconciling formats from many sources | A managed landing zone, common schemas and transformation tested against each source |
Cloud analytics platforms are strong at the second and third constraints, because they can scale ingestion and processing without a fixed hardware plan. They are not automatically cheaper at the first.
A cloud analytics platform is a set of decisions as much as a product. The design should answer how data is stored in its raw form, how it is transformed, who may query each layer and how long each layer is kept. Those answers affect security, cost and the speed at which new questions can be answered, so they should be written down before the first pipeline is built.
The data engineering and pipeline route is where the building of data pipelines is covered. The decisions above are the ones a buyer should expect to see in any proposal, whichever platform is chosen.
A large analytics store concentrates data that was previously spread across many systems, and that concentration changes the risk. Access should be granted by role and by layer, and each grant should be reviewed on a schedule rather than left in place after a project ends.
Personal data that is not needed for analysis should be pseudonymized or removed before it reaches the shared layer, because a broad analytics audience should not be able to see records it has no business reason to view. Query logs are part of the design too.
They show who asked what and how much it cost, and they are the evidence a security review or an audit often asks for.
Sharing outside the organization is the case that needs the most deliberate design. Publishing a dashboard to a partner or a regulator usually means exposing a subset of a reporting layer, with its own access rules and its own retention. Building that as a separate published layer, rather than granting external access to the shared store, keeps the internal model stable and the external exposure small.
A cloud analytics platform needs an operating owner in the same way any production system does. Pipelines fail, schemas change, costs drift and new users arrive with queries nobody tested. A practical operating model names one owner for the platform, one owner for each major data domain and a fortnightly review of cost and failures.
Changes to the platform itself, such as a new storage tier or a new processing engine, should go through the same change process as any other infrastructure. Where the internal team cannot carry that load, the managed IT services page lists cloud management among the areas OBLYTECH covers, and one workable split is for the internal team to keep the decisions about definitions and access.
Where the platform is being moved from an older estate, the cloud consulting and migration route covers cloud strategy, readiness assessment and migration planning, which come before that operating model is in place.
The choice of cloud platform for analytics is often driven by where the data already lives, the skills the team holds and the commercial relationship the business already has. The main hidden cost is data movement. Pulling large volumes out of one provider to process them in another is slow and expensive, and it is a common reason a multi-cloud analytics design fails to save money.
Placing processing close to the data is generally the safer default, and the cloud services overview describes the Azure, AWS and Google Cloud services OBLYTECH offers.
There is no threshold that settles it. The better test is whether the current platform fails to meet a time or cost requirement that the business has actually stated. Volume alone rarely justifies a distributed platform.
Mainly for the decisions that change within minutes. Management reporting is typically served well by hourly or daily loads, and paying for continuous processing across reports that do not need it is an avoidable expense.
It should move to cheaper storage under a retention rule. Keeping it in the fast query layer costs more without improving the answers.
Yes, and that is often the most practical path. The cloud layer handles the high-volume or varied sources, and the existing store continues to serve the reports that already work, until a planned migration is justified.
A productive first step is to measure what the current platform fails to do, in terms the business cares about: which report is late, which source cannot be joined and what the storage bill is growing by. Those three facts decide whether the answer is architecture, process or platform. Get in touch with OBLYTECH once you have those measurements for your data volume and analytics needs.
Choose a warehouse when your data is mostly structured and the questions are already known. Choose a data lake when you must keep large volumes of raw, mixed-format data before you know what will be asked of it. Many organizations end up with both, with the lake feeding the warehouse.
The two fail differently. A warehouse that is forced to absorb every raw file becomes slow to change. A lake with no curation turns into a store nobody trusts, where analysts spend their time guessing what each file means.
| Question | Points to a warehouse | Points to a lake |
|---|---|---|
| What do the sources look like? | Tables from business systems | Logs, documents, event streams, device output |
| Who reads the results? | Finance, operations, managers using dashboards | Data engineers and analysts who explore |
| How settled are the questions? | Reports are defined and repeated | Questions are still being discovered |
| What is the main risk? | Rigid models that resist new sources | Unowned, undocumented data |
List the five most important questions the business asks of its data. If four or more can be answered from structured tables, start with the warehouse and add a lake only when a real source cannot fit. If many depend on raw events or documents, start with the lake, but name an owner for each data set before anything is loaded.
The combined pattern works when each layer has a job. The lake holds raw copies, so transformations can be rerun. The warehouse holds the cleaned, modeled subset that reports rely on. Skipping the second step is a common mistake: teams point dashboards at raw storage, and each report then tends to carry its own private interpretation of the data.
Whichever you choose, check the inputs first. Duplicate customer records, missing keys and inconsistent codes will follow the data into either store. Our data readiness check describes how to test source quality, and the data strategy and consulting page covers agreeing ownership and priorities before a platform is bought. Once the model is settled, data warehousing and business intelligence explains the reporting side in more detail.
No. A lake stores data cheaply in its original form, while a warehouse organizes it for consistent reporting. They answer different needs, and many organizations that run a lake still keep a modeled layer for reports.
Copying an existing warehouse model into cloud storage usually moves the slowness with it. A cloud platform relaxes hardware limits, but it does not fix joins built around one reporting tool, or tables that have grown without a retention rule. The better order is to decide which questions the business needs answered, then rebuild only the layers those questions depend on.
Start with the ten or so questions that drive decisions each week. For each one, record how long the answer takes today, who runs it and which source tables it touches. That list is the test. A model that answers those questions quickly and predictably can stay largely as it is on a new platform. A model that needs a specialist to reconcile three versions of the same customer record will not improve by being hosted somewhere else.
Three signs suggest the model needs work before any platform decision:
When two or more of these are true, rebuilding the reporting layer is the first project and moving data is the second. Reversing that order usually means carrying the same slow queries onto the new platform. If the list also shows that no one owns the definition of each metric, an IT strategy and planning review can settle who decides before any build starts.
A phased approach keeps the risk small. Rebuild one reporting domain, such as service volumes, and run it beside the existing report for a full reporting cycle. Compare the totals line by line before switching users over, then move to the next domain. This shows which change improved an answer, and it gives each metric owner a chance to sign off before anyone relies on the new version. Teams that want help structuring that sequence can start with the data analytics services overview.
Guides from the Oblytech ServiceNow library that explain the practice behind this page.