Big data services

  • Forecast market changes and consumer demands with predictive analytics to swiftly adjust your business

  • Generate real-time insights from unstructured data to make smart and fast decisions and outpace competitors

  • Quickly pinpoint and resolve issues by implementing real-time data collection and analysis

  • Keep your data secure in a single source of truth and seamlessly manage even the biggest datasets

  • Get everyone in your company on the same page by connecting all internal and external data sources into one easy-to-use system

  • Visualize company achievements and analyze departmental performance with extensive reports and dashboards

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Big data solution services Yalantis provides

  • Big data consulting

    • Analysis of current data infrastructure

    • Evaluation of business goals for big data integration

    • Development of big data management strategy

    • Performing risk analysis for big data solution implementation

    • Estimating a big data project based on business goals and needs

  • Big data development services

    • Selecting the big data technology stack

    • Designing data architecture

    • Implementing suitable data governance procedures

    • Optimizing existing big data systems to handle increasing workload

  • Big data analytics services

    • Implementing a data analytics architecture layer

    • Selecting a big data analytics tool

    • Developing a data visualization and reporting dashboard

    • Data classification and labeling for simplified search

    • Exploratory data analysis and insights generation

  • Big data processing

    • Data preprocessing, cleansing, and transformation

    • Designing and implementing extract, transform, load (ETL) process

    • Selecting relevant big data sources

    • Collecting data from wearable and IoT devices

    • Selecting optimal tools for batch and stream processing

  • Big data solution reliability engineering

    • Testing data storage reliability and data processing speed

    • Performance testing of a big data solution

    • Load testing to assess how the system performs under increasing data volume

    • Defining big data system fault tolerance

  • Big data solution implementation

    • Integrating big data solutions into an existing business infrastructure

    • User onboarding flow

    • Knowledge transfer and training of an in-house technical team

    • Supporting and maintaining on-demand big data solution

  • Data integration services

    • Collecting data from multiple internal and external data sources

    • Extracting data from cloud and on-premises systems

    • Setting up data synchronization mechanisms

    • Integrating real-time data for quick decision-making

  • Data migration services

    • Setting up a risk-free data migration process

    • Extracting accurate and complete data

    • Selecting a suitable data loading approach

    • Ensuring compliance with industry-specific security requirements during migration

  • Data warehousing and data lake services

    • Selecting a data warehouse or data lake solution

    • Developing a data model

    • Developing data warehouse schema

    • Implementing security controls to secure data in the data lake or data warehouse

    • Deploying cloud or on-premises data warehouse

Generate insights from all relevant data sources and stay ahead of the industry

Build a single source of truth within your organization to get quick access to critical datasets and benefit from big data analytics.

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Big data services use cases Yalantis focuses on

  • Driving automated and intelligent decisions with AI/ML

    • Preparing data and engineering features for ML models

    • Training and evaluating ML models

    • Developing and optimizing deep learning models

    • Deploying ML models and integrating them with big data pipelines

    • Developing automated machine learning (AutoML) solutions

    • Creating natural language processing (NLP) solutions for text and audio analysis

  • Efficient processing of large datasets with distributive computing

    • Building cloud-based computing solutions

    • Designing and implementing scalable data architectures

    • Optimizing and tuning distributed system performance

  • Flexible and scalable data management with NoSQL databases

    • Selecting and implementing NoSQL databases

    • Modeling data and designing schemas

    • Administering and optimizing databases

    • Integrating NoSQL databases with existing applications

    • Migrating data from traditional databases

    • Developing custom APIs for interacting with NoSQL databases

  • Live data analysis with stream processing

    • Building pipelines for ingesting and processing data streams in real time (e.g., Apache Kafka, Apache Flink)

    • Implementing algorithms for filtering, aggregating, and transforming streaming data

    • Ingesting data streams from various sources (IoT devices, social media, logs, etc.)

    • Developing custom stream processing applications

    • Enhancing stream processing scalability and fault-tolerance

Industries Yalantis operates in as a big data company

  • Healthcare

    • Personalized medicine and treatment plans

    • Predictive analytics for early disease detection and intervention

    • Real-time monitoring of patient vitals using IoT devices

    • Definition of treatment patterns

    • Fraud detection in healthcare claims

  • Finance and banking

    • Financial market analysis

    • Fraud detection and prevention

    • Personalized investment recommendations

    • Credit scoring

    • Personalized banking services

    • Real-time stock trading analysis and prediction

  • Supply chain optimization

    • Demand forecasting and supply chain planning via historical data

    • Real-time inventory tracking and optimization with IoT sensor data

    • Predictive equipment maintenance using sensor data

    • Delivery route optimization using traffic and weather data

  • Retail

    • Personalized marketing and product recommendations

    • Demand forecasting and inventory optimization using sales data

    • Customer sentiment analysis from social media and review data

    • In-store customer behavior analysis using IoT sensor data

  • Energy and utilities

    • Real-time monitoring and energy consumption optimization

    • Efficient renewable energy management

    • Predictive maintenance of power grids and equipment

    • Assessment of electric vehicle (EV) adoption benefits

    • Customer segmentation and targeted energy-saving recommendations

  • Transportation and logistics

    • Delivery route optimization and real-time traffic analysis with GPS data

    • Predictive vehicle maintenance using sensor data

    • Demand forecasting and capacity planning using historical data

    • Real-time shipment tracking

    • Customer service and customer satisfaction analysis

  • Telecommunications

    • Analysis of usage data for planning capacity and identifying bottlenecks

    • Churn prediction

    • Targeted marketing

    • Detection of anomalies in network usage data

  • E-commerce

    • Product recommendations

    • Real-time price adjustments to optimize sales and inventory

    • Customer lifetime value analysis

    • Accurate demand forecasting

  • Real estate

    • Analysis of sales data, location information, and trends for accurate pricing

    • Neighborhood analysis

    • Market trend prediction

    • Lead matching

Results our clients get with big data services

  • Real-time insights for faster, smarter decisions

    • Make informed decisions with up-to-the-minute data, eliminating guesswork and operational delays.

    • Respond quickly to changing market conditions and customer needs to improve your service delivery.

  • Scalable and cost-effective system to manage big data

    • Accommodate growing data needs with optimal system performance and resource utilization.

    • Reduce costs with affordable, off-the-shelf data tools, customizing only when there’s validated business potential.

  • Built-in security and regulatory compliance

    • Ensure compliance with industry regulations, laws, and standards (GDPR, HIPAA, SOC2, ISO) to mitigate risk and protect sensitive data.

    • Implement customized security controls to monitor system performance and ensure timely security updates.

  • AI-ready data infrastructure

    • Lay the groundwork to integrate advanced analytics with AI/ML for predictive modeling to forecast demand and identify new revenue opportunities.

    • Anticipate future trends and customer behavior to stay ahead of the competition.

  • Maximized data ROI

    • Mine social media, consumer forums, and other platforms to discover valuable data assets and fuel innovative new products and services.

    • Transform raw data into structured, insightful, and actionable formats, creating revenue streams from previously untapped data sources.

  • Customized access to relevant data

    • Centralize all data into a single pool, providing each department or stakeholder with secure, customized access to needed data.

    • Empower every business user with customized dashboards and reports to reduce manual work.

Yalantis: A company with a proven portfolio

Supply chain big data analytics system

Explore how Yalantis implemented a technologically challenging and complex big data analytics solution for a US-based 3PL company.

See full case study

Data lake for a manufacturing company

Learn how Yalantis helped a manufacturing company set up a data lake solution to ensure supply chain visibility and optimize use of manufacturing data.

See full case study

Platform for real-time election data processing

Find out how we developed a
system for effective election data management to speed up calculation of voting results.

See full case study

Make confident decisions that propel your company forward

Partner with our big data experts to transform vast datasets into strategic advantages.

Discuss a big data project

Video reviews of our clients

Roy

Partner at RAKwireless

What triggered us was their remote collaboration practices as well as their experience in the IoT industry. Their strong technical experience helped us scale our platform and deliver great performance to our customers. 

Simon Jones

CIO in Healthcare

Yalantis has been a great fit for us because of their experience, responsiveness, value, and time to market. From the very start, they’ve been able to staff an effective development team in no time and perform as expected. 

Andrew Gazdecki

CEO at MicroAcquire

Working with Yalantis, you get their breadth of experience building hundreds of projects. Their expertise and knowledge were second to none. And that makes the difference between a good product and a great product.

Insights into our big data services

Product

BI and advanced analytics solutions for supply chain data analysis

Read a guide on BI and advanced analytics solutions for the supply chain. Learn how to implement each one and read about common tools and techniques for both.

How to

Guide to real-time data processing

Find out key stages of handling real-time big data efficiently, learn what tools and technologies to use to streamline this process.

How to

Central repository for your data: data mart, data warehouse, or data lake?

Learn the differences between a data warehouse, a data mart, and a data lake and pick the right data repository to cover your industry-specific needs.

Assess your business readiness for a big data solution

Count on Yalantis experts to prepare your business data infrastructure for seamless implementation of big data analytics.

Discuss details
FAQ

How would my company benefit from big data analytics?

Yalantis tailors big data services to each client’s unique data landscape, business objectives, and industry requirements. This unlocks a wide range of benefits:

  • Data-driven decisions at scale. Move beyond guesswork and make informed choices based on real-time insights and historical trends revealed through big data analysis.
  • Cost savings. Identify inefficiencies, reduce waste, and optimize resource allocation by leveraging big data insights.
  • New market opportunities, product innovations, and revenue streams. Uncover hidden patterns, trends, and correlations in business data.
  • Empowered workforce. Equip employees at all levels with self-service analytics tools to reduce manual effort, allowing your team to focus on high-value activities.
  • Enhanced customer experience. Gain a deeper understanding of customer behavior, preferences, and sentiments to deliver personalized experiences and improve customer satisfaction.
  • Competitive advantage. Use real-time data processing and advanced analytics to quickly adapt to changing market conditions and customer needs.

How does your company ensure the security and confidentiality of data at scale?

We employ a multi-layered approach to protect your sensitive information, which includes:

  • implementing industry-standard encryption protocols like AES-256 to secure data in transit and at rest
  • implementing strict role-based access controls and authentication mechanisms to ensure that only authorized personnel can access your data
  • adhering to relevant industry regulations, laws, and standards, such as the GDPR, HIPAA, and ISO 27001
  • conducting regular security audits and vulnerability assessments to identify and address potential risks
  • building big data solutions on secure, scalable cloud platforms like AWS and Azure, which offer robust security features and maintain strict compliance requirements
  • providing rigorous security awareness training to ensure our data and BI engineers follow best practices in handling sensitive data

What technologies and tools does your big data company work with?

Yalantis data science specialists select the most suitable tools to set up efficient extract, transform, load (ETL) processes, organize well-suited data storage, and deliver insightful data visualization. Here are some examples of tools we use for different types of big data services:

  1. Apache Spark, an open-source platform for fast in-memory data processing as well as batch and stream processing
  2. Apache Storm and Apache Samza, easy-to-use frameworks designed to support multiple programming languages for real-time stream processing
  3. Amazon Web Services (AWS) for data integration, storage, and visualization, including:
    – Amazon Data Exchange for accurate data collection from third-party services
    – Amazon S3 for scalable big data storage
    – Amazon QuickSight for ML-powered data visualization

How long does it take Yalantis to implement a custom big data product?

The implementation timeline for a big data solution varies depending on several factors:

  • Project scope. The volume and complexity of data involved can impact the project’s duration, typically ranging from three to nine months.
  • Project deadlines. If you have strict deadlines, we can allocate additional resources to accelerate development, but this may increase costs.
  • Business urgency. For pressing business issues to be solved with big data solution services, we offer an iterative approach that allows you to test and refine the solution gradually.

We always aim to deliver your big data solution promptly, but without compromising on quality. While this may occasionally extend timelines, the extra effort will result in a rock-solid solution that exceeds your expectations and drives business value.

What data types and data sources do you work with as a big data service provider?

We have experience working with all types of data, whether it’s neatly organized spreadsheets (structured) or messy social media posts (unstructured). We can also pull data from a vast range of sources to give you a complete picture of your business, including from:

  • IoT devices
  • third-party services
  • social media platforms
  • news websites
  • customer surveys
  • internal software (CRM, ERP, HRM)

Provide us with a list of your desired data sources and we’ll research additional relevant sources for your business and industry. We’ll then design an architecture that ensures accurate data aggregation, either in real time for immediate analysis and in bulk for storage and further use.

Does your big data company provide advanced analytics services on top of big data services?

Yes. The Yalantis big data services team can enhance your big data solution with advanced analytics services to maximize its value. Our process includes:

  1. researching and selecting advanced analytics tools that suit your business needs
  2. integrating these solutions into your architecture design (e.g., Amazon Athena for data lake analysis or Amazon SageMaker for predictive analytics)
  3. testing the performance of these solutions to ensure they perfectly fit into your workflow, enhancing decision-making and improving business operations

PRINCIPLES BIG DATA SERVICE PROVIDERS FOLLOW TO DEVELOP A REAL-TIME BIG DATA SOLUTION

As an experienced big data service provider, we follow five key principles when developing real-time big data systems. If you’re considering working with a big data software company, understanding these principles will give you a general idea of the development process.

#1: Understanding the business problem

First, we work closely with clients to identify their specific needs, goals, and pain points. By conducting a thorough audit of the client’s current systems, processes, and data landscape, we can better assess the project’s scope.

#2: Choosing tools and technologies for stream processing

Stream processing is crucial for real-time data analytics and artificial intelligence, as incoming data must be quickly analyzed to generate insights.

We use platforms like Apache Storm, Apache Spark, and Amazon Kinesis big data services to establish an efficient big data processing flow. In particular, Amazon Kinesis Data Firehouse is an ETL tool that captures, processes, transforms, and transfers large datasets directly to a data analytics service.

#3: Designing an architecture for real-time big data

As a big data services and technology company, Yalantis considers flexibility and scalability the key characteristics of a real-time big data architecture. With these non-functional requirements in mind, your big data system can handle high loads and adapt to shifting industry demands. The Yalantis team often works with event-driven architectures, which are common for such systems. They ensure high performance and process large numbers of requests.

#4: Establishing an advanced analytics process

Big data service providers integrate advanced analytics, artificial intelligence and machine learning capabilities into their solutions to derive meaningful insights from data. These technologies identify patterns, anomalies, and trends in real-time data streams. By applying machine learning algorithms, Yalantis can automate decision-making and gain deeper insights from corporate data across various sources.

#5: User-centered interface based on a comprehensive UX survey

Yalantis’ big data services also include a holistic approach to UI/UX design, ensuring that both technical and non-technical users can effectively use the system. We conduct UX surveys among future end users to define the most convenient flow, enabling users to easily navigate the system and generate critical business insights.

#6: System monitoring and timely alerts

As a reliable big data solutions company, Yalantis also implements a notification center into a big data analytics platform to proactively alert admins of any roadblocks that need attention. Round-the-clock and comprehensive solution monitoring and maintenance are features that differentiate Yalantis from other top big data companies.

WHAT’S BIG DATA AS A SERVICE (BDAAS)?

Big data as a service (BDaaS) is a cloud-based service model that includes a wide range of end-to-end big data services such as collecting large data sets, establishing data warehouses, and creating data lakes for scalable storage of large amounts of unstructured and structured data.

BDaaS provides businesses with access to the tools, infrastructure, and expertise needed to manage and analyze big data without the complexities and costs of building and maintaining on-premises infrastructure.

Yalantis has extensive experience working with BDaaS providers and integrating their services into our clients’ enterprise data projects. We have collaborated with leading BDaaS platforms powered by Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) to deliver comprehensive big data solutions.

For instance, in one of our projects, we used AWS BDaaS offerings to help a logistics company process and analyze vast amounts of data from various sources, including IoT devices, customer logs, and supply chain systems. Yalantis data engineering and business intelligence (BI) experts used the following AWS services:

  • Amazon S3 for data lake storage
  • Amazon QuickSight for data visualization and analysis
  • Amazon Identity and Access Management for data security and governance

As a result, the client got a custom big data analytics platform for efficiently analyzing business data.

Here’s a quick breakdown of services and benefits Yalantis offers for big data projects when partnering with BDaaS providers:

  • Comprehensive big data ecosystem solutions: Yalantis leverages BDaaS providers to deliver end-to-end big data services, from collection of various data types to storage with data warehousing.
  • Scalability and flexibility: BDaaS solutions are designed to scale seamlessly as your data volume grows, allowing you to adapt to changing business needs.
  • Reduced costs: By eliminating the need for upfront hardware and software investments, BDaaS offers a cost-effective way to leverage big data analytics.
  • Enhanced expertise and support: Yalantis’ data engineers and BI experts integrate relevant BDaaS services to ensure you get the most out of your big data initiatives, offering ongoing support and access to data science expertise.

How exactly does BDaaS enable these business benefits?

Let’s see in detail the key aspects of BDaaS and how your business can benefit from it:

Scalability to make on-demand changes whenever needed

A BDaaS provider can offer highly scalable solutions, allowing your business to easily accommodate growing data volumes or a growing user base. You can scale your solution up or down as needed without hassle or hardware setup. As such, cloud-based big data services save time and money while maintaining a flexible software solution.

Cost-efficiency with a subscription-based approach

With big data as a service, you don’t usually need substantial upfront investments in hardware, software, or IT staff. You pay only for what you use on a subscription or pay-as-you-go basis, making it easier to stop paying for unnecessary features or support.

BDaaS is cost-effective for businesses of all sizes since:

  • small companies may lack the capacity to develop big data solutions from scratch
  • large organizations can reduce operating expenses and save money for urgent business needs

Reduced maintenance overhead

Managing on-premises big data infrastructure can be resource-intensive, and that’s one reason why organizations look for a trusted big data solutions company. BDaaS takes the load off your team for diverse IT maintenance tasks like hardware and software updates, allowing your IT team to focus on more strategic initiatives.

Speed and agility to accelerate time to market

Top big data companies offer preconfigured data environments and tools, which enables rapid deployment of big data solutions. This means a shorter time to market for your big data products and faster value derivation compared to setting up an on-premises data management flow.

Such agility allows your business to quickly respond to changing data requirements and market conditions, giving you a competitive edge.

Automated data management to save time

BDaaS solutions include automated data storage, processing, and management capabilities for real-time data ingestion, prompt transformation, accurate cleansing, and secure storage. Having a reliable big data service partner constantly available saves time compared to maintaining full-blown big data technologies and infrastructure on your own.

Analytics and actionable insights to encourage business process visibility

Big data as a service platforms provide access to powerful analytics tools and machine learning capabilities. Compared to custom software development, with BDaaS, you won’t get stuck in a long research phase to choose the right advanced analytics services for your business.

Such tools come built into your BDaaS platform and can help your business extract valuable insights from your data, leading to data-driven decision-making and improved business outcomes. However, a reliable software partner is key to fine-tune your data and advanced analytics tools and establish a proper analytics process.

Security and compliance to meet industry standards and user expectations

Reputable BDaaS providers invest heavily in strong security and compliance measures to protect your data. Since BDaaS is cloud-based, big data service providers offer robust security controls, solid data encryption, and compliance certifications, which can help you meet industry-specific regulatory requirements.

Ensuring end users’ security expectations is also crucial, as you need to assure users that their data is well-protected.

Global reach for efficient big data service troubleshooting

BDaaS providers often have data and service centers in multiple regions, allowing your business to analyze and store data closer to end users or easily comply with local data requirements.

With big data as a service, your business can be more flexible. Even if you expand to different countries, you’ll have the same access to your big data management flow and can guarantee uninterrupted services. A mature BDaaS provider ensures you aren’t limited by location or access to IT staff or resources.

Collaboration and accessibility to make big data services simple

BDaaS solutions foster cross-platform collaboration among data analysts and other teams with stakeholders, as data and analytics tools are easily accessible via a web platform.

This way, you can enable productive remote work and global collaboration, which is especially beneficial for large enterprises that need to ensure all departments are working towards common goals.

Customization to maintain digital brand identity

Cooperating with a big data as a service provider doesn’t necessarily mean that you will be stuck with prebuilt functionality. BDaaS providers often offer a wide array of managed services and configurations, allowing you to tailor your big data management platform to your specific needs.

You can choose services and expertise that align with your data and analytics requirements and modify the system over time by adding more resources.