Big data processing
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Make balanced strategic decisions by collecting, storing, and analyzing structured, semi-structured, and unstructured data
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Enable real-time big data analytics with distributed computing and parallel processing techniques for massive datasets
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Increase customer loyalty and retention by continuously gathering customer insights from multiple sources
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Maximize the value of innovative technologies such as IoT, AI, and ML by developing a custom big data processing platform
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Reduce data infrastructure costs and complexity with suited cloud platforms and open-source technologies
Implementation results of big data processing
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UP TO 10x
faster data processing speeds with optimized frameworks
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UP TO 50%
reduction in latency for real-time analytics
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UP TO 40%
cost savings through cloud-based big data solutions
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UP TO 100%
compliance with industry regulations
Big data processing services Yalantis provides
Bring clarity and visibility to your data workflows. We help you discern and prioritize valuable big data sources and datasets for accurate analytics. With our expertise and ongoing guidance, you can launch a risk-free big data solution that helps you better understand your operations and customers.
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Real-time data processing
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Analyzing sources that produce real-time data
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Setting up real-time data aggregation
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Processing continuous data flow
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Integrating big data storage system
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Enabling real-time big data analytics
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Batch data processing
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Selecting multiple data sources
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Automating ETL processes
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Transferring data into a data warehouse or data lake
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Enabling data mining
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Scaling batch processing infrastructure
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Data pipeline development
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Assessing data infrastructure
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Setting up data integration tools
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Designing scalable pipeline architecture
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Ensuring pipeline monitoring and error handling
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Big data analytics enablement
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Selecting big data analytics tools
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Configuring analytics workflows
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Generating custom reports and dashboards
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Enabling predictive analytics features
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Cloud-based big data solutions
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Choosing a suitable cloud provider
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Designing cloud infrastructure for big data storage and processing
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Configuring cloud-based data access and security controls
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Implementing cost-optimization strategies for cloud usage
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Data governance and compliance management
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Defining data policies and standards
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Implementing data quality management tools
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Ensuring compliance with regulatory requirements
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Enabling audit trails and access control mechanisms
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Data processing
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Cleaning and preprocessing raw data
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Implementing advanced data transformation techniques
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Setting up distributed processing systems (e.g., Hadoop, Spark)
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Ensuring high-speed processing through optimization
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Post-processing support and optimization
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Monitoring big data solution performance
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Optimizing system scalability
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Conducting regular maintenance and upgrades
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Providing technical support and troubleshooting assistance
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Real-time data processing
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Analyzing sources that produce real-time data
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Setting up real-time data aggregation
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Processing continuous data flow
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Integrating big data storage system
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Enabling real-time big data analytics
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Batch data processing
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Selecting multiple data sources
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Automating ETL processes
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Transferring data into a data warehouse or data lake
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Enabling data mining
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Scaling batch processing infrastructure
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Data pipeline development
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Assessing data infrastructure
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Setting up data integration tools
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Designing scalable pipeline architecture
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Ensuring pipeline monitoring and error handling
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Big data analytics enablement
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Selecting big data analytics tools
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Configuring analytics workflows
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Generating custom reports and dashboards
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Enabling predictive analytics features
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Cloud-based big data solutions
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Choosing a suitable cloud provider
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Designing cloud infrastructure for big data storage and processing
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Configuring cloud-based data access and security controls
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Implementing cost-optimization strategies for cloud usage
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Data governance and compliance management
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Defining data policies and standards
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Implementing data quality management tools
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Ensuring compliance with regulatory requirements
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Enabling audit trails and access control mechanisms
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Data processing
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Cleaning and preprocessing raw data
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Implementing advanced data transformation techniques
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Setting up distributed processing systems (e.g., Hadoop, Spark)
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Ensuring high-speed processing through optimization
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Post-processing support and optimization
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Monitoring big data solution performance
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Optimizing system scalability
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Conducting regular maintenance and upgrades
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Providing technical support and troubleshooting assistance
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Develop a cost-efficient big data processing and analytics solution
Assess your data infrastructure and compose a detailed roadmap for advanced big data analysis.
Processing of big data across industries
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Finance
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Prediction of stock price movements
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Financial risk management
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Trading strategies optimization
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Detection of fraudulent transactions
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Healthcare
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Personalized patient treatment
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Clinical risk management
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Disease recognition and prediction
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Patient monitoring
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Drug recommendations
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Pharmaceutical research
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Telemedicine
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Manufacturing
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Predictive maintenance
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Material waste reduction
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Production quality control
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Prevention of production delays
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Real-time anomaly recognition
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Supply chain
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Transportation management
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Optimization of fuel consumption
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Delivery route optimization
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Supply and demand forecasting
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Traffic management
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Real-time vehicle tracking
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Yalantis: A company with a proven portfolio of processing big data
Rather than sitting on big data—act on it
Combine structured data and unstructured datasets in a single source of truth and start generating insights.
Insights into Yalantis’ big data processing services
An enterprise data governance framework: Solve your biggest challenges with data security, quality, compliance
Is data chaos costing your business growth and innovation? Discover how to tackle data challenges around security, consistency, quality, and compliance with a single solution.
Instant analysis of time-critical data: Real-time data streaming use cases, examples, and benefits
How to ensure efficient real-time big data analytics
Find out key stages of handling real-time big data efficiently, learn what tools and technologies to use to streamline this process.
Data Modernisation:
Unlock your business data’s potential with a modern, cost-effective system that makes data accessible, actionable, and easy for your team to embrace.
How do you build a real-time data processing solution for time-sensitive use cases?
As the first step, the data team helps you identify which data is time-sensitive and how to prepare your data architecture and infrastructure for its effective collection and use. We set up a real-time data streaming flow to enable immediate data ingestion from multiple sources that produce time-sensitive data. Then, data collected from these sources is transferred for data storage, processed, and loaded in the selected destination, such as real-time analytics services or data warehouses.
To handle big data processes efficiently, our data engineers employ advanced big data technologies and frameworks, such as Apache Flink or Apache Kafka. These tools ensure seamless processing of big data, including both structured and unstructured data from sources such as IoT sensors, gateways, and routers, allowing for quick decision-making and improved customer service.
How do you ensure compliance and security during big data processing?
We begin our cooperation with you by establishing a comprehensive data governance framework that indicates rules for data collection, distribution, and use. The next step would be to integrate efficient security controls and tools that can automatically verify security and compliance of big data processing.
When using AWS services for the processing of big data, we enable AWS Identity and Access Management (IAM) and Amazon Cognito to make sure only authorized users can access your sensitive data. AWS Artifact and AWS Audit Manager gather relevant data for regular audits that correspond to your particular industry regulations, laws, and standards. AWS Security Hub centralizes security alerts and helps companies visualize and improve security posture during the processing of big data.
What big data frameworks and tools do you work with?
We have experience with a wide selection of open-source big data tools and depending on your current business needs, available resources, and a level of digital maturity, we select the most fitting tech stack.
For instance, in big data batch processing, we make use of Amazon EMR, AWS Batch, AWS Flue, Azure HDInsigh, and Google Cloud Dataproc. Stream processing solutions such as Apache Spark, Storm, Flink, and Kafka ensure the continuous flow of data from various sources, making them vital for real-time and near-real-time big data processes. We can select appropriate tools to efficiently handle structured data, unstructured, and semi-structured data.
How long does it take to set up a big data processing pipeline?
It depends on your infrastructure readiness and data team proficiency. But the latter is even more important than the first. For instance, to speed up data infrastructure deployment, Yalantis offers DevOps accelerator tools with pre-built solutions that allow for quick infrastructure roll-out and a four times faster development process. By choosing an experienced team skilled in big data processes, you can develop a robust and efficient data processing solution that saves both time and costs. Our solutions ensure compatibility with diverse data models to address your unique requirements.
Do you provide ongoing support and optimization after implementation?
We help our clients maintain the performance, scalability, and security of their big data processing solutions. Our data team offers on-call and on-demand big data consulting and support services. We can also assist you in implementing advanced analytics tools with AI and ML algorithms for enhanced big data processes and generating predictive and prescriptive insights. We also focus on improving data quality, ensuring the accuracy and reliability of the information used for decision-making.
Benefits and use cases of big data processing for enterprises
Big data processing benefits medium, large, and very large companies across industries. Any organization that handles large datasets can gain significant advantages by adopting a custom data processing solution. You need big data processing if you:
- struggle to resolve complex business challenges and uncover their root causes
- face difficulties in collecting, accessing, and analyzing diverse data types, including raw data, unstructured data, and semi-structured data
- plan to integrate IoT devices and advanced analytics tools
- intend to improve operational and strategic decision-making
For instance, financial companies can adopt big data processing and analytics to identify market trends, predict stock price movements, and speed up decision-making. Healthcare organizations can facilitate remote patient monitoring by collecting and analyzing data from medical IoT devices. Manufacturing companies can gain real-time visibility of their production floors and improve the quality of goods. Supply chain businesses can optimize fuel consumption and delivery routes to decrease transportation costs and still deliver orders on time.
Below are the expected benefits of implementing a big data processing solution that collects, stores, and prepares relevant data for all-around analytics and fuels business decision-making.
Next-level decision-making
If you rely on data rather than guesswork to make business decisions, your company already stands out among the competition. But what if you used big data? The quality of your business decisions would be even higher. Big data processing involves handling large volumes of data collected from various valuable sources. These datasets come in different formats at the required speed (batch processing, real-time, and near real-time data streaming). If you master big data and channel it into advanced business intelligence tools to generate data-intensive reports and dashboards, your decision-making will skyrocket. Analyzing both structured data and unstructured data offers businesses access to unique and valuable insights, increasing their competitive edge.
Improved operational efficiency
Collecting and processing big data from a variety of internal and external sources allows businesses to quickly identify issues and their root causes.For instance, a logistics company using big data processes can optimize routes by analyzing traffic conditions, driver behaviors, and sensor data from vehicles.
Enhanced risk management
Take one step forward in risk management with big data processing. You can use big data not only to identify and mitigate risks but also to prioritize, predict, and prevent them. With big data processing, your company can compose prescriptive risk mitigation plans and maintain business continuity.
Replacing traditional data processing tools with advanced big data solutions ensures improved risk detection and management. All-around risk and security management processes based on big data allow you to safeguard your IT infrastructure from cyberattacks. An AI-powered big data processing solution can even suggest security measures that might be useful when dealing with data of different sensitivity levels. Financial organizations, for example, use big data processing to identify fraudulent transactions.
Optimized expenses
Even though investing in a custom big data solution can be costly, the ROI is quick. By consolidating large datasets of structured data and unstructured data in a single source of truth, you can increase process visibility across your company departments. You’ll get a chance to discover ineffective workflows, redundant procedures, productivity issues, outdated tools, and time-consuming manual tasks. Realizing and incrementally fixing these problems can help you significantly reduce expenses and change their course to more important initiatives such as enterprise-wide digital transformation.
Big data processing use cases
Personalized customer experiences with smart recommendations and targeting
When businesses know their customers, users, or patients well, they will attract only more. Adopting big data solutions opens the door to your customers’ hearts by gathering every step of their interaction with your services or products. Analyzing data collected from medical IoT devices, patients’ health records, and medical images such as MRI scans helps healthcare practitioners make accurate diagnoses and quickly prescribe suitable treatment. E-commerce companies can analyze their web clickstream data and customer behavior data collected from in-store sensors to offer personalized shopping experiences. There are numerous ways of improving customer experiences with the help of big data processing and analytics that will fit your business model and needs.
Data-driven product development and innovation
A big data processing solution allows enterprises to analyze real-time market data and customer feedback to develop new products and services that resonate with their target audience. Plus, big data solutions combine well with cutting-edge technologies such as AI and machine learning algorithms to:
- provide action plans
- generate innovative ideas
- suggest product marketing campaigns
- offer implementation recommendations.
Before you launch a new product or service, a big data solution can analyze competitors’ data and help you stay ahead of the competition. For instance, a retailer can consider manufacturing a new drink but needs to validate this idea first, discover which consumers would buy it, gain stakeholders’ buy-in, and analyze other similar products. Without streamlined big data processing and analysis, the preparation of this product would take years, while automated big data analysis can speed up the launch up to months.
Predictive maintenance
Equipment failure causes downtime in every sphere, particularly in manufacturing and healthcare. Sudden outages of medical equipment can be life-threatening to the patients. Downtime on the production floor slows down the assembly line and delays production. ATM failures can cause immense disruption for banks and decrease customer satisfaction with the services.
Keeping track of equipment health with the help of sensors and continuously gathering and analyzing this data allows organizations to maintain uninterrupted service delivery. By enhancing big data processing solutions with AI and machine learning models, businesses across industries can create prescriptions for optimal equipment maintenance.
Real-time fraud detection and prevention
Fraudulent activities and cyberattacks can break any business’s reputation in a matter of hours. They can harm businesses in finance, e-commerce, healthcare, and other industries. Secure and streamlined big data processing and analytics flow enables businesses to analyze vast amounts of transactional structured data, user behavior, and historical patterns to identify anomalies and suspicious actions. With the help of custom AI and machine learning models, organizations can flag unusual transactions or activities for review and prevent fraud before it occurs.
- Finance: monitor transactions to detect credit card fraud, money laundering, or insider trading in real time.
- E-commerce: identify fraudulent orders or account takeovers by analyzing shopping patterns and device usage.
- Healthcare: detect insurance fraud or improper billing practices by cross-referencing patient records and claims.
Big data processing and analytics improve operational security but also safeguard businesses and customers from financial and reputational damage.
Establish a big data processing flow to elevate data analytics
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Lisa Panchenko
Senior Engagement Manager
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