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Big Data

Unlocking the value of your data with scalable big data solutions that drive insight and informed decision-making.

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Big DataScale your Big Data services with our nearshore talent.

Our Big Data services services already power over 200 active engagements. We typically land our teams within 2 weeks, so you can start shipping top quality software, fast.

 
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500+ companies rely on our top 1% tech talent.

Big Data Services We Provide

Business Intelligence and Analytics

Find opportunities, mitigate risks and optimize performance in real-time. Our big data scientists create custom analytics solutions that can extract insights from huge datasets as they are being generated. For your analytics stack, we use Power BI and Tableau for visualization, Apache Spark for real-time processing, and TensorFlow and Scikit-learn for machine learning. For scalable data warehousing, we use Snowflake and Google BigQuery.

Data Integration and ETL

Turn disparate data sources into one unified, high-quality dataset–even in the most complex data environments. Our integration and ETL solutions give you data that’s consistent, accurate and ready for real-time insights. So you can eliminate inefficiencies and speed up decision-making. We use Apache NiFi and Talend for seamless extraction, transformation and loading (ETL). Our experts use Airflow and dbt for complex workflow orchestration. We also use Amazon Redshift and Azure Synapse for querying in our data warehousing solutions.

Business Intelligence and Analytics

Find opportunities, mitigate risks and optimize performance in real-time. Our big data scientists create custom analytics solutions that can extract insights from huge datasets as they are being generated. For your analytics stack, we use Power BI and Tableau for visualization, Apache Spark for real-time processing, and TensorFlow and Scikit-learn for machine learning. For scalable data warehousing, we use Snowflake and Google BigQuery.

Data Integration

Struggling to make sense of data spread across multiple platforms? We specialize in capturing, collecting and moving massive amounts of structured and unstructured data from real-time streams, databases or third-party APIs into your data architecture. Our experts use Apache Kafka and AWS Kinesis for real-time streaming ingestion, Apache Flume for log data aggregation and Google Cloud Dataflow for both batch and stream processing. With these tools we build fast and scalable data pipelines that power timely insights and informed decisions.

Big Data Platform Development

Process, store and analyze huge amounts of data at high speed and efficiency. Whether you need to power predictive modeling, advanced data analytics or AI-driven applications, we architect platforms that can handle real-time analytics to large-scale batch processing. Our developers use Hadoop and Apache Spark to build scalable, distributed systems and integrate HDFS, Amazon S3 and Google Cloud Storage for secure, high-throughput data storage. For querying we use Presto for fast ad-hoc querying and Apache Hive for large-scale batch queries. Our experts also use Docker and Kubernetes for agile, optimized performance.

Data Storage Solutions

Support real-time data streams, large-scale archives and high-speed transactions. We design and implement scalable storage systems that can handle huge amounts of structured and unstructured data. Our solutions store it securely, retrieve it quickly and manage it with minimal downtime or errors. Our experts use technologies like Amazon S3, Google Cloud Storage and HDFS for distributed storage, for durability and fault tolerance. We also implement advanced data replication and backup strategies using tools like Apache Cassandra for distributed NoSQL databases and PostgreSQL for relational databases.

Data Visualization

Turn complex datasets into clear, actionable insights. We specialize in converting raw data into interactive, easy-to-understand visualizations. Whether you want to track performance metrics, identify market trends or uncover hidden patterns our visualizations help you make faster, data-driven decisions. We use leading tools like Tableau, Power BI and D3.js to create dynamic dashboards, charts and graphs. With just a few clicks you can drill down into details or view high-level summaries. By integrating real-time data streams our visualizations are always up to date.

AI/Machine Learning Data Solutions

Get intelligent systems that process massive datasets and learn from them. Our AI-driven solutions deliver actionable insights that allow you to automate routine processes, forecast market trends and even build recommendation engines. We use powerful frameworks like TensorFlow, PyTorch and Scikit-learn to develop machine-learning models. Our data scientists then use them to extract patterns, build predictive algorithms and automate decision-making processes. Our expertise also includes tools like Google AI and AWS SageMaker for scalable model training, deployment and continuous monitoring.

ENGAGEMENT MODELS

HOW WE HELP

Key Things to Know About Big Data

Big data is transforming many industries by providing insights, improving decision-making and innovation. Here are the key industries where our big data solutions make the most impact:

  • Healthcare: to optimize patient care, predictive analytics, drug discovery, personalized medicine
  • Finance and Banking: to detect fraud, manage risk, personalize financial products, algorithmic trading, regulatory compliance
  • Retail and E-commerce: to analyze customer behavior, recommend products, inventory management, personalize marketing
  • Telecommunications: to optimize networks, customer churn prediction, customer service through real-time data analysis
  • Manufacturing: to optimize supply chain, improve production processes, predict equipment failure through sensor data
  • Government and Public Sector: to inform policy, urban planning, public safety
  • Energy and Utilities: to analyze energy consumption, grid management, operational efficiency
  • Media and Entertainment: to personalize content recommendations, advertising strategies
  • Travel and Hospitality: to personalize customer experience, pricing strategies, guest experience
  • Automotive: to design vehicles, predictive maintenance, autonomous driving

By using big data businesses can unlock many powerful benefits that drive growth, efficiency and innovation. Here are the top benefits for businesses today:

  • Better decision making: Big data provides real-time insights from massive datasets, so you can make more accurate data-driven decisions. Long term strategy and day to day operations.
  • Customer experience: by analysing customer behavior and preferences you can deliver personalized interactions and recommendations that boost retention, satisfaction and loyalty
  • Operational efficiency: big data optimizes internal processes, reduces costs and improves workflow efficiency. Predictive analytics further helps to anticipate issues and minimise operational disruption
  • Risk management and fraud detection: big data analytics is key to identifying risks and detecting fraud especially in finance and cybersecurity. Early detection helps businesses reduce risk and comply with regulations
  • Innovation and product development: big data insights reveal emerging trends and customer needs. These insights help businesses innovate and develop products that meet the market.
  • Revenue growth: Big data gives businesses access to customer insights, optimize pricing and discover new revenue streams.
  • Scalability and agility: big data solutions can handle growing amounts of data so businesses can grow and adapt to changing market conditions.

Our experts have been working alongside in-house teams for over a decade.

  • React
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  • React
  • Angular
  • Node.js
  • Java
  • C++
  • .NET
  • Vue.js
  • JavaScript
  • Python
  • Golang
  • Swift
  • Figma
  • Adobe
  • C#
  • PHP
  • iOS
  • Android
  • Python
  • WordPress
  • Swift
  • Figma
  • Adobe
  • C#
  • PHP
  • iOS
  • Android
  • Python
  • WordPress
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How to start with Us

Our process. Simple, seamless, streamlined.

Step 1
Join exploration call.

Tell us more about your business on a discovery call. We’ll discuss team structure and approach, success criteria, timescale, budget, and required skill sets to see how we can help.

Step 2
Discuss solution and team structure.

In a matter of days, we will finalize your project specifications, agree on an engagement model, select and onboard your team.

Step 3
Get started and track performance.

Once we’ve agreed on milestones, we’ll immediately get to work. We’ll track progress, report updates, and continuously adapt to your needs.

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Frequently Asked Questions (FAQ)

Big data can be used for a wide range of applications. These include predictive analytics, customer behavior analysis, decision making, supply chain optimization and fraud detection. No wonder big data solutions are used across various industries from healthcare and finance to retail and manufacturing.

A big data project typically involves data collection, data cleaning, data storage, processing and analysis. To manage large datasets developers use tools like Hadoop and Spark and cloud platforms like AWS and Azure. This also includes building pipelines to process and visualize data for insights and deploying models for predictive analytics or machine learning.

Structured data is highly organized and formatted in a way that’s easily searchable and analyzable. This includes databases with rows and columns. Unstructured data lacks a specific format. It includes text documents, videos and social media posts. Big data technologies can process both to derive meaningful insights.

 

Big data technologies distribute the data processing workload across multiple servers or nodes. Platforms like Hadoop and cloud services like AWS and Azure allow businesses to scale their data storage and processing capabilities as data volumes grow. This ensures performance even with large datasets.

 

Real-time data processing in big data means analyzing and acting on data as it is generated rather than after it is stored. This is important for applications like fraud detection, online recommendations and IoT data analysis. Our devs use tools like Apache Kafka, Apache Flink and AWS Kinesis to handle real-time data processing and make informed decisions based on up-to-the-minute information.

 

Big data helps businesses understand their customers by analyzing their preferences and behavior. By collecting data from multiple sources, companies can personalize interactions to fit the specific needs of their customers. For example, e-commerce companies can recommend products based on past purchases or browsing habits. This personalization makes customers feel valued and understood.

Analyzing customer feedback allows businesses to quickly address concerns, improve services and predict customer needs. This leads to higher customer satisfaction and long term loyalty.

Data science professionals are key to big data projects. They design algorithms and statistical models to extract valuable insights from large datasets. A data scientist’s job is to clean, organize and analyze data to ensure accuracy and relevance. They often work with tools like Python, R and machine learning platforms to do their analysis. 

Data scientists also work with other departments like IT and marketing to develop customized strategies such as personalized marketing campaigns and predictive maintenance models. Their data analysis expertise helps these departments make informed decisions and optimize their operations.

Machine learning algorithms need a lot of data to identify patterns and make accurate predictions and big data provides the large datasets that machine learning models need to work effectively.

With big data, models can analyze real-world information from multiple sources like customer behavior, market trends or sensor data. The more data the machine learning system processes, the more accurate and reliable the predictions become.

Big data also supports real-time learning. Models can update and improve as new data becomes available, which helps businesses automate tasks, forecast trends and make data driven decisions.

Data quality is maintained by data cleansing where duplicates are removed, errors are corrected and missing information is filled in to ensure the dataset is accurate and reliable. Then developers do validation checks to ensure the data is consistent and accurate across different sources. They then monitor data in real-time with tools like Apache NiFi or Talend and flag any inconsistencies for immediate correction. This ensures the data is accurate and useful throughout its lifecycle.

Remember, maintaining data quality is not a one-time effort but an ongoing, iterative process. To keep these standards high, we recommend that organizations implement strong data governance policies, use automated monitoring tools, and regularly audit their data processes.

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