Big Data and High Load Services

As a software development service provider, SPG helps businesses creating a state-of-art bespoke Big Data Services and High Load solutions. We develop solutions that are scalable and high-performing. Their organisation-tailored features enable businesses to process and analyse their data in real-time, while deriving valuable insights, and make data-driven decisions.

Big Data and High Load solutions are a type of cloud-based solutions that provide organisations with the ability to manage, analyse and process large volumes of data while handling high traffic loads. These solutions require scalability and high-performance computing infrastructure in order to handle eff iciently the 3Vs of Big Data: volume, velocity, and variety. That’s why creating a bespoke big data solution require highly-skilled experts.

In today’s data-driven economy, businesses are collecting and processing massive amounts of data to gain valuable insights and improve their operations. As a result, the demand for powerful and scalable solutions that can handle high volumes of data has increased significantly. Both Big Data and High Load concepts refer to the technologies, tools, and techniques used to manage and process large and complex datasets, and the infrastructure required to support them.

Big Data Solutions

Our Big Data Services

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Experts in Big Data Architecture

SPG employs the teams of highly skilled professionals with a proven track record of delivering successful enterprise-grade big data processing solutions. Their expertise spans across various big data instruments, including Apache Hadoop, Apache Spark, and Apache Kafka, which enables them to design and implement scalable, fault-tolerant, and cost-effective big data architectures that meet their clients’ unique requirements. With a deep understanding of distributed computing and cloud computing, SPG delivers innovative solutions that help organisations extract maximum value from their accumulated data by gaining insights, making forecasts, improving decision making and increasing overall business efficiency. SPG is proud to be an officially registered partner with Databricks as well.

Big Data Tools:

Apache Hadoop

MongoDB

Apache Spark

PowerBI

Solr

Tableau

High Load Tools

Apache Kafka

Redis

ElasticSearch

Kubernetes

Pub/Sub

Nginx

HAProxy

Cloudflare

ClickHouse

RabbitMQ

Docker

We’ll Transform Your Data Into Valuable Insights

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Big Data Sources

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Big Data Platforms
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Mobile Device Data
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IoT/Sensor Data
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Transactional Customer Data
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Web/Online Data
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Big Data Architecture’s Types

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Batch Architecture

Batch architecture is a popular approach in Big Data services that involves processing large volumes of data in batches, usually during off-peak hours. It involves storing data in distributed file systems like HDFS and processing them using batch processing frameworks like Apache Hadoop or Apache Spark. Batch architecture is ideal for processing large volumes of data that do not require real-time processing, such as data warehousing and batch reporting. It enables cost-effective and scalable processing of Big Data while providing fault tolerance and data redundancy. Batch processes large data volumes cost-effectively without real-time processing needs.

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Lambda and Kappa Architectures

Lambda and Kappa architectures are two popular Big Data processing approaches that address the challenges of processing large volumes of data. The Lambda architecture involves processing data in two separate paths - batch and real-time, while Kappa architecture focuses solely on real-time processing. Both architectures aim to provide scalable and fault-tolerant solutions for processing and analyzing Big Data. The choice between them depends on specific use cases and requirements, with Lambda architecture being more suitable for historical data analysis, and Kappa architecture being better suited for real-time data processing.

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Streaming Architecture

Streaming architecture is a data processing approach that enables real-time processing of Big Data. It involves processing data in small, continuous streams as they are generated or received, rather than processing them in large batches. Streaming architectures use distributed stream processing frameworks like Apache Flink or Apache Kafka Streams to process data in real-time, providing near-instant insights and enabling real-time decision making. Streaming architecture is ideal for use cases that require real-time processing, such as fraud detection, real-time analytics, and event processing. It provides a highly scalable and fault-tolerant solution for processing and analyzing Big Data in real-time.

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Hybrid Architecture

Hybrid architecture is a Big Data processing approach that combines batch and streaming processing to provide a flexible and scalable solution for data processing. It involves leveraging the strengths of both batch and streaming architectures to process data efficiently. Hybrid architecture is ideal for use cases that require both real-time insights and historical analysis of large data sets. It enables businesses to process data in real-time while also providing the ability to analyze large amounts of historical data. Hybrid architecture can be implemented using various Big Data processing frameworks like Apache Spark, Apache Flink, and Apache Kafka.

BigData Platforms We Use

We cater to the needs of organisations for storing, processing, and analysing large volumes of data for reporting and analysis purposes and can leverage capabilities of the most mainstream platforms:

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Databricks

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Amazon Redshift

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Teradata DWH

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Oracle Autonomous Warehouse

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Google BigQuery

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Cloudera

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Microsoft Azure

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Snowflake

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SAP Data Warehouse

Case Studies

SPG are proud of the work we do. From SMEs to large corporations looking for web developers in the UK, we are the trusted partners of hundreds of businesses — both UK-wide and internationally — who put their confidence in our experienced programming specialists. If you are interested in some of our past projects, have a look at our featured case studies!

Web Applications Case Studies

What Our Clients Say

When it comes to serving customers, there is never really a silver bullet. Our success is the direct result of working hard to find the right approach for every one of our specific partners.

“At our organization, they’re known for being insanely productive. If we give them an assignment, we know it’ll be completed on our end before we’re even ready to review it. That’s really why we keep coming back year after year."

VP of Engineering, Hospitality Company. Miami, Florida

“They’re technically proficient, delivering eloquent solutions to all of our problems. They’re a team of clever, critical thinkers. I can’t think of anything that I would’ve changed about their service. They were absolutely fantastic."

CEO, Iron.io. Las Vegas, Nevada

"Their team philosophy combines reliable, customized software solutions for everyone and an individual approach to each client with unrivaled offshore value" — read the full review.

Kate Gross, a journalist for fixthephoto.com

“They took a hands-on approach and suggested site improvements, which speaks volumes to their commitment. I would not have a business without the work this company did. We now have hundreds of users, including over 80 paying ones!"

Founder & CEO, Sequitur News LLC. Champaign, Illinois

“This is the best experience I’ve had with an outside vendor. They’re collaborative and insightful, providing insight and expertise to improve the final result. They’re also flexible and timely."

Performance Manager, Nokia. Budapest, Hungary

Our Development Process

Our development process in Big Data incorporates industry-leading practices, starting with a thorough understanding of your business needs and objectives. We use Agile methodologies, adapted to your company’s unique requirements, to ensure efficient and effective project delivery. Our team of experts employs the latest Big Data technologies, frameworks, and tools to develop scalable, fault-tolerant, and cost-effective solutions that help you gain insights and extract value from your data.

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