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
Big data solution services Cloud Hawk 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 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.
Big data services use cases Cloud Hawk 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 Cloud Hawk 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.
Cloud Hawk: A company with a proven portfolio
Make confident decisions that propel your company forward
Partner with our big data experts to transform vast datasets into strategic advantages.
Insights into our big data services
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.
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.
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 Cloud Hawk experts to prepare your business data infrastructure for seamless implementation of big data analytics.
How would my company benefit from big data analytics?
Cloud Hawk 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?
Cloud Hawk 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:
- Apache Spark, an open-source platform for fast in-memory data processing as well as batch and stream processing
- Apache Storm and Apache Samza, easy-to-use frameworks designed to support multiple programming languages for real-time stream processing
- 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 Cloud Hawk 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 Cloud Hawk big data services team can enhance your big data solution with advanced analytics services to maximize its value. Our process includes:
- researching and selecting advanced analytics tools that suit your business needs
- integrating these solutions into your architecture design (e.g., Amazon Athena for data lake analysis or Amazon SageMaker for predictive analytics)
- 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 Provides 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, Cloud Hawk 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 Cloud Hawk 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, Cloud Hawk can automate decision-making and gain deeper insights from corporate data across various sources (machine learning use cases).
#5: User-centered interface based on a comprehensive UX survey
Cloud Hawk’ 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, Cloud Hawk 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 Cloud Hawk 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 (data lake use cases).
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.
Cloud Hawk 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 AmazonWeb 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. Cloud Hawk 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 Cloud Hawk offers as anadvanced analytics solutions company for big data projects when partnering with BDaaS providers:
- Comprehensive big data ecosystem solutions: Cloud Hawk 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: Cloud Hawk’ 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.
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