When does your company need Big Data specialists?

Companies are generating increasingly large amounts of information. Transactions, applications, IoT devices, digital platforms, enterprise systems, logs, sensors, and user behavior continuously produce data.

However, having a lot of data does not necessarily mean having a Big Data problem.

A traditional database can perfectly manage the needs of many organizations. Incorporating technologies like Apache Spark, Kafka, or distributed architectures without a real need can introduce more complexity than value. The scenario changes when the volume, velocity, or variety of information exceeds the capacity of conventional architectures.

Processing millions of events, analyzing information almost in real-time, or combining large amounts of data from multiple systems requires different infrastructure. And also professionals capable of designing it.

Therefore, before hiring Big Data specialists, there is a more important question:

Does your company really have a problem that requires Big Data?

In this guide, we analyze when these technologies start to add value, which professionals are involved, and what capabilities a company should look for before building a Big Data architecture.

What is Big Data?

Big Data refers to data sets whose scale, velocity, or complexity make efficient processing difficult using certain traditional tools and architectures.

It is traditionally explained through different characteristics, among which the following stand out:

• Volume: Large amounts of information generated and stored.

• Velocity: Data produced or processed continuously and, in certain cases, almost in real-time.

• Variety: Information coming from multiple sources and in different formats.

These dimensions are often supplemented by concepts like veracity and value. Because storing enormous amounts of information has little utility if the data is not reliable or cannot be transformed into useful knowledge for the business.

Big Data does not simply mean having a lot of data

This distinction is fundamental.

A company may have millions of records and not need a Big Data architecture. Modern databases, Cloud Data Warehouses, and other technologies can manage considerable amounts of information without introducing more complex distributed systems.

The problem arises when the existing infrastructure begins to encounter limitations.

For example:

• Processes take hours to complete.

• Volume is continuously growing.

• There are multiple sources of information.

• Real-time analysis is needed.

• Queries no longer respond adequately.

• Systems need to process large streams of events.

• Traditional infrastructure is difficult to scale.

It is then that Big Data technologies can start to make sense.

When does a company need a Big Data architecture?

There is no exact number of gigabytes or terabytes that determines when Big Data begins. The need depends on the problem.

When traditional processing no longer scales

If processing information requires too much time or too many resources, it may be necessary to distribute the work among multiple systems. This is where technologies specifically designed for distributed processing come into play.

When you need to process information in real-time

Some organizations cannot wait until the next day to analyze their data.

Fraud, telemetry, monitoring, recommendations, or certain operational processes require almost immediate information. In these scenarios, data streaming architectures become especially important.

When there are many different sources

Applications, devices, CRM, ERP, logs, and external services can generate information with completely different structures. Integrating and processing all that data may require a specialized architecture.

When Data Science or Machine Learning need large datasets

Certain models need to process significant amounts of information. Big Data infrastructure can provide the necessary capacity to prepare and process those datasets.

What does a Big Data specialist do?

A Big Data specialist designs, builds, and maintains systems capable of efficiently processing large amounts of information. In many organizations, this responsibility falls to Data Engineers specialized in distributed architectures.

Their functions may include:

• Designing data architectures.

• Building pipelines.

• Distributed processing.

• Data streaming.

• Source integration.

• Processing optimization.

• Data Lakes.

• ETL and ELT.

• Monitoring.

• Automation.

• Integration with Cloud platforms.

Therefore, Big Data Engineer and Data Engineer may have overlapping areas.

The difference is usually found mainly in the scale and complexity of the architectures they work with.

Fundamental technologies in Big Data

The ecosystem has evolved considerably, and not all companies need to use the same tools.

Apache Spark

is one of the most relevant technologies for processing large amounts of data in a distributed manner. It can be used for batch processing, data analysis, and different workloads related to Data Engineering and Machine Learning.

Apache Kafka

plays an important role in event-driven architectures and streaming data processing. It allows handling large streams of events continuously generated by applications and systems.

For example:

Applications → Kafka → Processing → Data Lake → Analytics

Apache Hadoop

played a fundamental role in the historical evolution of Big Data and continues to exist in certain architectures.

However, the modern ecosystem has evolved considerably towards Cloud solutions and managed platforms. Therefore, experience in Big Data should not be evaluated simply by asking if a candidate knows Hadoop.

Databricks

provides a platform for working with Data Engineering, Analytics, Machine Learning, and modern data architectures.

It can be especially relevant in organizations that need to combine different workloads over large volumes of information.

Snowflake

Snowflake is a Cloud data platform that allows storing, processing, and analyzing large amounts of information. It is part of many modern architectures where organizations seek to separate storage and computing capacity.

Big Data in AWS, Azure, and Google Cloud

The evolution of Cloud has profoundly transformed this area.

Previously, building Big Data infrastructure could involve managing large clusters of servers. Currently, AWS, Microsoft Azure, and Google Cloud provide numerous managed services for storage, streaming, processing, and Analytics. This allows building architectures capable of scaling without directly managing all the physical infrastructure.

However, Cloud does not eliminate the need for architecture.

A poorly designed solution can generate:

• Unnecessary costs.

• Data duplication.

• Slow processes.

• Governance issues.

• Difficult-to-maintain dependencies.

Technology simplifies part of the infrastructure, but architectural decisions remain fundamental.

Batch processing vs real-time processing

This difference helps to understand many Big Data architectures.

Batch Processing

Information is processed in groups. For example, a company may process all transactions generated during the day every morning. It is appropriate when an immediate response is not needed.

Stream Processing

Information is processed as it is generated.

For example:

Event → Kafka → Processing → Action

It can be used in fraud detection, monitoring, IoT, personalization, or systems where response time is critical. Not everything needs to be real-time. In fact, using streaming when a batch process is sufficient can unnecessarily increase complexity.

Big Data vs Data Engineering: what is the difference?

This distinction is especially important for our SEO architecture.

Data Engineering is a broader discipline related to building systems that collect, transform, store, and distribute data.

Big Data appears when those systems need to work with levels of volume, velocity, or complexity that require specific architectures and tools.

We can see it this way:

Data Engineering → discipline

Big Data → scale and architecture problem

Therefore, not all Data Engineers necessarily work with Big Data.

But a large part of Big Data architectures does need professionals with solid knowledge of Data Engineering.

Big Data vs Business Intelligence

They also serve different objectives.

Big Data focuses primarily on the infrastructure and capacity needed to process large amounts of information.

Business Intelligence uses data to generate reports, dashboards, and indicators that facilitate decision-making.

A possible architecture could be:

Sources → Big Data / Data Engineering → Data Warehouse → Business Intelligence → Decision

They are complementary layers.

Big Data vs Machine Learning

Big Data is also not synonymous with Machine Learning.

Machine Learning uses data to develop models capable of identifying patterns or generating predictions. Big Data can provide the necessary infrastructure to process large datasets that subsequently feed those models.

For example:

Millions of events → Big Data → Dataset → Machine Learning → Prediction

However, many Machine Learning projects work perfectly well without needing a Big Data infrastructure.

What profiles does a Big Data project need?

Depending on the complexity, different professionals may be involved.

• Big Data Engineer: Builds distributed systems and pipelines prepared for large volumes of information.

• Data Engineer: Develops the integration, transformation, and storage infrastructure.

• Data Architect: Defines the overall architecture and the technologies that the platform will use.

• Cloud Data Engineer: Works on data services available in AWS, Azure, or Google Cloud.

• Data Platform Engineer: Builds and maintains internal platforms used by other data teams.

A complex project may also need specialists in Cloud, DevOps, Database, Machine Learning, or Business Intelligence.

When to outsource Big Data specialists?

Building a specialized internal team can be complex when the need is linked to a specific project.

For example:

• Building a Data Lake.

• Migration to Cloud.

• Implementing streaming.

• Modernizing a data platform.

• Scaling pipelines.

• Implementing Databricks.

• Developing a distributed architecture.

In these scenarios, IT Outsourcing allows incorporating one or more specialists to reinforce existing capabilities. When the project requires multiple disciplines, Team as a Service allows building a complete team.

For example:

Data Architect + Big Data Engineers + Cloud Engineer + DevOps + Data Analyst.

The composition can evolve as the architecture changes.

How lateam incorporates Big Data specialists

At lateam, we help companies incorporate specialists in Data and Big Data through IT Outsourcing and Team as a Service models.

Before starting the selection process, we analyze the sources of information, volume and velocity of the data, current architecture, Cloud platform, and project objectives. From there, professionals with experience in technologies like Apache Spark, Apache Kafka, Databricks, Snowflake, Python, SQL, AWS, Azure, and Google Cloud can be identified.

However, the technological selection should come after understanding the problem. Because a good Big Data architecture is not necessarily the one that uses the most technologies.

It is the one that solves the data problem with the least necessary complexity.

Frequently asked questions about Big Data

What is Big Data?

Big Data refers to scenarios where the volume, velocity, or complexity of data requires architectures and technologies capable of processing them efficiently.

When does a company need Big Data?

When conventional infrastructure begins to show limitations for processing large volumes of information, multiple sources, or data streams with high-speed requirements.

What does a Big Data Engineer do?

Designs and maintains systems capable of processing large amounts of information using Data Engineering, distributed processing, and streaming technologies.

What technologies does a Big Data specialist use?

Depending on the architecture, they may work with Apache Spark, Apache Kafka, Databricks, Snowflake, Python, SQL, and specialized services from AWS, Azure, or Google Cloud.

What is the difference between Big Data and Data Engineering?

Data Engineering is the discipline responsible for building data infrastructures and pipelines. Big Data represents scenarios where scale or complexity requires specialized architectures.

Are Big Data and Machine Learning the same?

No. Big Data relates to the storage and processing of large amounts of information, while Machine Learning uses data to train models and make predictions or classifications.

Do all companies need Big Data?

No. Many organizations can perfectly meet their needs with conventional databases and data platforms. Incorporating a Big Data architecture without a real need can increase costs and complexity.

Big Data should not be a technological goal.

It should be a response to a real data problem.

When the volume, velocity, or complexity of information exceeds the capacity of the existing architecture, technologies like Spark, Kafka, Databricks, or Cloud platforms can provide the necessary scalability. But adding these technologies without a clear need can also generate costly and difficult-to-maintain architectures.

Therefore, before hiring Big Data specialists, a company should determine what problem it is trying to solve, what volume it needs to process, and what speed the business truly requires.

The question is not:

Do we need Big Data?

The correct question is:

Can our current architecture process the data we need, at the speed and scale demanded by the business?

If the answer is no, then it is probably time to strengthen Data Engineering capacity.

Build a data architecture ready to scale

At lateam, we help companies incorporate Data Engineers, Big Data Engineers, and other data specialists through flexible IT Outsourcing and Team as a Service models. Whether you need a specialist or a complete team, we can help you identify the right profiles for your architecture and objectives.