Big Data Analytics Services

Get insights from huge amounts of data, fast and at scale

Experience unparalleled speed and scalability with Big Data solutions. Our expertise in Google BigQuery and MongoDB ensures your data is not only big but smart – driving efficient decisions and robust growth.

Our Big Data Analytics Services

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Customized Big Data Solutions for Your Business

We recognize that every business faces unique challenges and opportunities in managing and analyzing large datasets. Our Big Data Analytics services, powered by industry leaders like Google BigQuery and MongoDB, are crafted to address your specific needs. From enhancing your current data operations to building new big data infrastructures, our experts support you at every step.

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Building Advanced Data Infrastructures

Our seasoned team excels in configuring robust cloud and PaaS big data environments that harness the full potential of Google BigQuery and MongoDB. These platforms are renowned for their ability to process and analyze large volumes of data quickly and efficiently. Whether you’re starting anew or scaling existing databases, we ensure a seamless setup and integration tailored to your data demands.

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A detailed view of a data engineering workflow on a screen, showcasing a visual ETL tool like Alteryx, representing Witanalytica's customized data engineering and big data solutions.

Data Governance and Scalability

Data integrity and scalability are paramount. We implement rigorous data governance frameworks to ensure your data is accurate, consistent, and compliant with international standards. Our scalable solutions grow with your business, ensuring you can handle increasing data loads without compromising performance.

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Seamless Data Integration

Our big data services ensure comprehensive integration of your data sources, providing a unified view that supports consistent and reliable analytics. From ERP systems to CRM applications, our integrations are designed to offer a cohesive data environment that serves as a reliable foundation for all your business needs.

Sales Use Case

Breaking Down business siloes with big data analytics

Our big data analytics services begin by thoroughly understanding and aligning with your business objectives.

Recognizing that every company faces unique challenges and holds distinct opportunities, we ensure our solutions are precisely tailored to meet your strategic goals.

Focusing on scalability and efficiency, we develop advanced big data infrastructures that can grow with your business.

Leveraging leading technologies like Google BigQuery and MongoDB, we design systems that not only handle vast volumes of data but also integrate seamlessly across various platforms. 

What Our Customers Say

Your goals, our expertise

We start from your strategic objectives and work our way back to the right mix of solutions and technologies, not the other way round

our strategic approach to big data analytics deployment

Understand Business and Reporting Needs

Our data engineering process begins with a thorough understanding of your business needs and business intelligence (BI) reporting requirements. This step involves identifying the key metrics, refresh frequency, and the specific reports needed to drive decision-making within your organization.

Identify and Access Data Sources

We then identify the right data sources and obtain access to the underlying systems. This involves collaboration with various departments to ensure all relevant data is considered, whether it's from CRM, ERP, web analytics, or other sources.

Extract, Transform, and Load (ETL)

In this step, we extract the data from the identified sources, transform it to fit the required format and structure, and load it into appropriate storage solutions such as databases, data lakes, and data warehouses. Our ETL process is designed to handle both structured and unstructured data, ensuring comprehensive data integration.

Check for Retroactive Updates

We test for retroactive data updates to ensure data integrity. This involves identifying any overlap periods and performing upserts or replacements as necessary to maintain accurate and up-to-date datasets.

Create Data Assets and Products

We focus on creating reusable data assets and products. These are designed to be utilized multiple times across various departments, maximizing their value and utility. This includes creating standardized data sets, dashboards, and other BI tools.

Develop APIs and Web Interfaces

To encourage data consumption, we develop APIs and web interfaces. These interfaces allow for easy access to the data products, whether through reports, API endpoints, files, emails, or other means. This ensures that the data is readily available to all stakeholders, enhancing operational efficiency.

Ongoing Monitoring and Maintenance

Finally, we provide ongoing monitoring and maintenance of the data infrastructure. This includes regular updates, performance optimization, and ensuring compliance with data governance standards. Our goal is to maintain a high-quality and high availability data environment that supports continuous business growth and innovation.

When Do You Need Our Big Data Analytics Services

When you need to handle large volumes of data to uncover hidden patterns, improve decision-making, optimize operations, and gain a competitive advantage in increasingly data-driven markets.

Why Hire Witanalytica As Your Big Data Consulting Company

Our Big Data Pricing Models

Icon depicting a stopwatch and documents, representing the Time and Material flexible billing option for data analytics services.

Time and Material

Suited for projects where the scope may vary, this pay-as-you-go option offers the flexibility to adjust requirements as your project evolves. We’ll work with you to estimate the effort involved and ensure transparency and fairness in billing.

Icon illustrating a piggy bank and calendar, signifying the retainer fee model for consistent analytics support.

Retainer Fee

If you have ongoing analytics needs, our retainer service ensures dedicated support for a set number of hours each month. It’s a great way to secure our team’s availability without the commitment of a full-time hire.

Icon featuring a programmer, denoting the dedicated resources model for data analytics services.

Dedicated Resources

For businesses that anticipate a consistent, high-volume workload, we offer the option of dedicated resources. This model provides you with a team or individual fully focused on your data analytics needs for a sustained period, offering stability and deep integration with your operations.




MongoDB is a flexible NoSQL database platform, popular for its document-oriented storage and high scalability. Deployed as a Platform as a Service (PaaS), it allows developers to manage and analyze diverse data sets efficiently with very low costs.

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Big Query

BigQuery is a fully-managed, serverless data warehouse solution offered by Google Cloud that enables super-fast SQL queries over large datasets. This cloud-based service excels in ease of use, scalability, and maintenance, providing powerful data analytics capabilities.

Not sure which one is the best for your use case?

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Marketing Automation

Learn how we streamlined publisher engagement and maximized profits for a top-tier US affiliate marketing platform using advanced marketing automation and targeted offer recommendations.

Read Case Study »

Frequently Asked Questions (FAQs)

What is big data analytics?

“Big data” typically refers to datasets that are so large, complex, and rapidly changing that they are difficult to manage and analyze using traditional data processing tools. The term is not only about the size but also encompasses the variety, velocity, and veracity of the data. Here are the key characteristics that usually define big data:

  1. Volume: The size of the data sets is massive, often ranging from terabytes to petabytes and beyond.
  2. Velocity: The speed at which new data is generated and needs to be processed is extremely high. This can include real-time data processing requirements.
  3. Variety: Big data comes in various forms—structured, semi-structured, and unstructured. This includes everything from texts and multimedia to logs and sensors.
  4. Veracity: The quality and accuracy of data can vary greatly, impacting the insights derived from it.
  5. Value: The ability to turn large datasets into valuable insights that can be used to make informed decisions.

In practical terms, big data often involves the integration of data from multiple sources that provides businesses the opportunity to uncover hidden patterns, unknown correlations, and other insights that can lead to more informed decision-making and strategic business moves.

What is the role of big data in a company?

Big data plays a crucial role in enabling companies to enhance operations, provide better customer service, drive personalized marketing strategies, and ultimately increase profitability by making data-driven decisions.

How does big data analytics enable decision making?

Big data analytics provides insights from extensive data sources at high speed, allowing companies to make informed decisions quickly. It helps identify trends, predict customer behavior, and optimize business processes.

What does a big data analytics consulting company do?

A big data analytics consulting company helps organizations strategize and implement big data technologies. We provide expertise in data management, analytics, and the extraction of actionable insights to drive strategic decisions and innovations.

What are some common challenges businesses face when adopting big data analytics?

Common challenges include data integration from disparate sources, ensuring data quality and governance, scaling data infrastructure, lack of skilled personnel, and extracting meaningful insights from massive datasets.

You should consider contracting big data analytics consulting services when:
  1. Data Volume Exceeds Internal Capabilities: When the volume, velocity, or variety of data exceeds the processing capabilities of the company’s existing tools and personnel.

  2. Specialized Expertise Required: When specialized knowledge is needed to manage, analyze, or extract value from large datasets, particularly in sophisticated fields like machine learning, artificial intelligence, and predictive analytics.

  3. Strategic Decision-Making: To leverage data-driven insights for strategic decision-making, such as entering new markets, optimizing operations, or personalizing customer experiences.

  4. Efficiency and Cost Reduction: When seeking to improve operational efficiency, reduce costs, or enhance productivity through optimized data handling and analysis.

  5. Scaling Needs: When the company is scaling up operations and needs to ensure their data infrastructure can accommodate growth without performance bottlenecks.

  6. Compliance and Security: To ensure compliance with data protection regulations and to enhance the security of data handling processes.

  7. Competitive Advantage: To maintain or gain a competitive edge in the industry by utilizing the latest in data analytics technology and methodologies.

Deciding whether to hire an in-house big data analytics team or to outsource to a data analytics services agency depends on several factors:

Consider Hiring an In-house Team If:

  1. Ongoing Need: Your data analytics needs are continuous and central to your business operations.
  2. Data Sensitivity: You handle highly sensitive or proprietary data that requires strict control and security measures.
  3. Custom Solutions: You require bespoke analytics solutions tightly integrated with your business processes.
  4. Strategic Capability: Building expertise in big data analytics is strategic to your business model or competitive advantage.
  5. Budget: You have the budget to support full-time salaries, technology investments, and ongoing training for specialized personnel.

Consider Outsourcing to an Agency If:

  1. Cost Efficiency: Budget constraints make it less feasible to hire full-time specialized staff.
  2. Scalability: You need the flexibility to scale operations up or down based on project demands without long-term commitments.
  3. Expertise on Demand: You require immediate access to a broad range of expertise and advanced technologies that are not core to your business.
  4. Risk Mitigation: Outsourcing can reduce the risk and investment required to set up and maintain an analytics infrastructure.
  5. Project Specificity: Your analytics needs are project-specific or you require occasional deep dives into data insights that in-house teams cannot justify economically.

Combined Approach:

Many companies find a hybrid approach effective, where strategic aspects of data analytics are managed in-house while more specialized or project-specific tasks are outsourced. This allows businesses to maintain control over core competencies while still leveraging external expertise for advanced analytics and technology.

Our services mainly cater to mid sized companies that have reached an inflection point where they can no longer effectively manage their operations, sales, marketing using Excel and Google Sheets. We cover the full spectrum of services such as data analytics consulting, data engineering, database administration, business intelligence and data science.

We can take care of the entire process of setting up an effective data analytics architecture from scratch or alternatively, however we can also help with targeted modular services.

For example, some of our customers have reached out to us when they needed to speed up the development and delivery of dashboards. As such, we have helped them with Business Intelligence development while they chose to keep data engineering capabilities in house.

We have also had customers contract us to build all the necessary pipelines and infrastructure to build a data warehouse from scratch.

We have extensive experience of managing PaaS deployments of MongoDB on Linux servers which, by our measurements, is one of the cheapest way to do Big Data Analytics for mid and large scale projects.

And we’re not talking 10-20% cheaper than cloud alternatives, we are taking 4-6 times cheaper.

We have worked with Affiliate Marketing, FMCG, Healthcare, Manufacturing, Transportation and Airlines, Logistics, SaaS and IT, Media and Advertising, Telecomm, Retail and Dealership companies.

Absolutely, please navigate to our Case Studies page and browse through the examples we have showcased there.

Our go to tools are MongoDB and Big Query, but we also use data lakes and serverless databases when needed.

Here is a complete list of technologies we use:


AWS Lake Formation, Azure Data Lake Storage, Google Cloud Storage

Data Warehouses

Amazon Redshift, Azure Synapse Analytics, Google BigQuery, Snowflake


Amazon Aurora, Amazon RDS, Azure SQL Database, Google Cloud SQL, PostgreSQL, MariaDB, DynamoDB, Cosmos DB, Big Query, MongoDB, Cassandra, Elasticsearch

Choosing the right cloud provider depends largely on your industry and the tools you are already using. Here’s a tailored recommendation based on common industry practices:

Advertising and Digital MarketingGoogle Cloud Platform (GCP): Many advertising and digital marketing companies prefer GCP because it integrates seamlessly with Google Analytics 4 (GA4), Looker Studio, and BigQuery. If your business heavily relies on these tools, GCP provides a familiar and powerful ecosystem for your data needs.

E-commerceAmazon Web Services (AWS): AWS offers a wide array of services like Amazon Redshift for data warehousing and AWS Lambda for serverless computing. If your e-commerce platform is already utilizing tools like Amazon SageMaker for machine learning or Amazon RDS for databases, AWS would be a natural fit.

Finance and BankingMicrosoft Azure: Azure excels in providing enterprise-grade security and compliance, making it ideal for finance and banking sectors. If you are using Microsoft products like PowerBI for business intelligence or Azure SQL Database, migrating to Azure ensures seamless integration and robust data governance.

Healthcare: Microsoft Azure or AWS: Both Azure and AWS offer strong compliance with healthcare regulations (HIPAA). Azure is particularly strong if you are using Microsoft products for patient management systems, while AWS offers comprehensive healthcare-specific services and scalability.

Manufacturing and Logistics: AWS or Azure: AWS provides extensive IoT and machine learning capabilities, which are crucial for manufacturing and logistics optimization. Azure is a strong contender if your operations are deeply integrated with Microsoft products and you require advanced analytics through Azure Synapse Analytics.

Technology and SaaS: AWS or GCP: Both AWS and GCP are excellent for technology companies. AWS provides a broad range of services for building and scaling applications, while GCP is ideal if your products leverage Google’s AI and machine learning tools.

Your choice of cloud provider should align with your current toolset and industry-specific needs. Each cloud provider offers unique advantages, and the best choice will depend on your existing technology stack and the specific requirements of your business operations. If you need further personalized guidance, we’re here to help you assess your current setup and make the optimal decision for your cloud migration journey.

Our approach to big data adoption is grounded in a thorough understanding of your business’s key processes and data requirements. Here’s a detailed look at how we ensure robust and reliable data engineering solutions:

  1. Initial Consultation and Understanding Key Processes: We begin with in-depth discussions with business users and stakeholders to grasp the critical business processes that need data support. This involves understanding the key performance indicators (KPIs) and the data required to drive strategic decision-making.
  2. Requirements Gathering from BI and Business Analysts: We collaborate closely with Business Intelligence (BI) teams and business analysts to gather detailed requirements. This helps us identify the necessary data sources, ensuring our solutions align with reporting needs and business objectives.
  3. Data Source Identification and Integration: We identify and connect to the relevant data sources, which can range from databases, APIs, to third-party systems. Special attention is given to API data fetching, where we focus on versioning and maintaining historical data. This ensures that even if APIs do not store historical changes, our data capturing process retains a comprehensive history.
  4. Data Granularity and Aggregation: Our methodology involves bringing in data at the most granular level possible. We then create multiple warehouse layers to aggregate this data appropriately for various reporting frequencies—daily, weekly, and quarterly. This layered approach ensures flexibility and accuracy in reporting.
  5. ETL Development and Automation: We design and develop efficient ETL (Extract, Transform, Load) processes to handle data ingestion and transformation. Our ETL pipelines are built to ensure data is processed accurately and efficiently, ready for analysis and reporting.
  6. Quality Checks and Validation: Rigorous quality checks are performed to validate that the data aligns with the source systems and meets the predefined requirements. We ensure that our engineered data supports the necessary BI and reporting needs accurately.
  7. Logging and Monitoring: To ensure reliability, we implement comprehensive logging and monitoring processes. This proactive approach helps us detect and resolve issues before they impact the business, ensuring that ETLs run smoothly and data is always accurate.
  8. User Training and Documentation: We provide thorough documentation and training to ensure that users can effectively utilize the new data systems. Our goal is to empower your team to leverage the data for actionable insights and informed decision-making.

By prioritizing low-cost, serverless, and open-source solutions whenever possible, we ensure that our data engineering services are not only effective but also cost-efficient. Our focus on creating reusable data assets and products across different departments fosters consistency and operational efficiency, enabling your organization to make data-driven decisions with confidence.

We understand that data privacy and security are paramount. Here’s how we safeguard your data:

  • Ownership and Control: When we develop your data infrastructure, you have complete ownership and control over the architecture, systems, software, and—most critically—the data itself.
  • In-Ecosystem Work: Our team operates within your infrastructure, avoiding any data transfer outside your secured ecosystem, ensuring data residency and sovereignty.
  • Advanced Security Measures:
    • We implement and use SSH keys and VPNs for secure connections.
    • IP whitelisting and OAuth APIs are standard practice for controlled access.
    • Data encryption both in transit and at rest to protect your information.
    • Frequent password rotations to maintain security integrity.
  • Compliance and Standards:
    • Our processes are designed to align with GDPR and CCPA.
    • We readily adapt to meet any specific requirements of your internal security policies.

Apart from implementing widely know best cybersecurity practices, currently we do not have specialized cyber security personnel which is why we recommend and welcome 3rd party or your inhouse resources to regularly test our infrastructures.

Additionally, we understand the importance of accountability and are willing to explore obtaining insurance coverage that addresses potential damages directly attributable to our services. While we are dedicated to the highest standards of excellence and vigilance, we also recognize the necessity of defining liability.

In the unlikely event of damages and unless specifically insured for, our liability is capped at the total amount billed for our services in the preceding six months of our collaboration. This provision is part of our commitment to transparency and mutual trust in our business relationships.

Your data’s security is our top priority, and we commit to maintaining the highest standards of cybersecurity practices.

If you’re considering our big data consulting services, know that we prioritize a partnership approach to pricing. We want to ensure that our services align with your goals and provide clear value. Here’s an outline of how our pricing models can work for you:

  1. Time and Material: Suited for projects where the scope may vary, this pay-as-you-go option offers the flexibility to adjust requirements as your project evolves. We’ll work with you to estimate the effort involved and ensure transparency and fairness in billing.

  2. Retainer Fee: If you have ongoing analytics needs, our retainer service ensures dedicated support for a set number of hours each month. It’s a great way to secure our team’s availability without the commitment of a full-time hire.

  3. Dedicated Resource: For businesses that anticipate a consistent, high-volume workload, we offer the option of dedicated resources. This model provides you with a team or individual fully focused on your business intelligence needs for a sustained period, offering stability and deep integration with your operations.

We understand that each business’s needs are unique, and we’re committed to providing a pricing structure that reflects that.

For a detailed quote that’s tailored to your business’s specific data analytics requirements, please don’t hesitate to reach out to us. Our team is ready to discuss your objectives and how we can align our services for the best outcome.

The duration of a big data project can range from 6 to 24 months, depending on its scope and complexity.

Our approach is highly flexible, allowing us to scale resources to meet project deadlines effectively. While our focus has shifted towards long-term collaborations, our core mission is to deliver tailored, impactful data analytics services that nurture lasting partnerships.

It’s important to recognize that a big data architecture is a living organism that constantly evolves. As businesses grow and adapt, there will always be new data sources to integrate and new insights to uncover, making the big data architecture an ever-expanding asset for informed decision-making.

Engaging with our big data consulting and development services is a simple and straight forward process, ensuring we align with your business needs every step of the way.

Here’s how it works:

Before Contract Engagement

  1. Initial Consultation: It all starts with booking a meeting where we discuss your business objectives and how our consulting services can align with your goals. We conduct a detailed discussion to understand your business strategy and objectives. This helps us explain and show how data can be transformed into actionable insights for your organization.

  2. Tailored Proposal: Based on our discovery meeting, we draft a custom proposal outlining the approach, services offered, and the estimated impact on your business.

  3. Contract Finalization: After reviewing the proposal with you and incorporating any feedback, we finalize the terms and sign the contract, setting the stage for our collaboration.

After Contract Engagement

  1. Onboarding & Data Integration: Our team sets up the necessary infrastructure for data ingestion, processing, and reporting, ensuring a smooth start to our partnership.

  2. Solution Implementation: We implement the best data analytics solution for your needs that can also include setting up infrastructure, integrations, and the development any agreed-upon dashboards.

  3. Training & Capacity Building: To ensure you get the most out of our services, we provide comprehensive training for your staff on the new systems and tools.

  4. Ongoing Support & Optimization: We offer continuous support, including performance monitoring and optimization, to ensure the solutions evolve with your business.

For more detailed information and to get started with transforming your data into strategic assets, contact us for a personalized consultation.

Absolutely, we encourage you to read the Testimonials section from this page.

At Witanalytica, staying at the forefront of big data is central to our approach. We actively keep ourselves informed through various channels, including industry news, to ensure we’re aware of the latest developments.

Our team personally tests emerging technologies, often being among the first to access new tools and features through early sign-ups and beta programs. We also delve into product releases and updates, ensuring that we understand the capabilities and applications of new technologies. Beyond external research, we dedicate time for internal projects, experimentation, and training.

This hands-on approach allows us to not only stay updated but also to critically evaluate and integrate new technologies and methods into our solutions, ensuring our clients always benefit from cutting-edge analytics.

At Witanalytica, ensuring the accuracy and reliability of our data analysis is foundational to our approach. We adhere to stringent data quality standards, focusing on accuracy, completeness, consistency, and reliability.

Our process involves rigorous data validation techniques to minimize errors. We tackle common data quality challenges, such as incomplete data, duplicates, data integration issues, and outdated information, by implementing best practices like setting mandatory fields, using data cleaning tools, standardizing data across systems, and establishing regular data refresh schedules.

To maintain the highest data quality, we also ensure data security, assign clear accountability and ownership of data sets, and conduct regular data audits. This comprehensive approach not only mitigates the risk of inaccurate insights but also supports informed decision-making and maintains our clients’ trust.

For a deeper dive into our data quality assurance practices and the importance of high-quality data in analytics, feel free to read more in our detailed article: Data Quality: How to Ensure Your Data Analytics Deliver Accurate Insights

After a project’s completion, our support at Witanalytica doesn’t just end. We actively monitor the dashboards and datasets for alerts and triggers to ensure continuous, smooth operation.

Maintenance of existing data pipelines takes precedence, ensuring they perform optimally before we embark on developing new ones. We offer flexible support options tailored to your needs — from a fixed maintenance fee to time-and-material billing for any necessary adjustments or maintenance. This approach allows us to swiftly adapt to changes, such as updates in data sources to ensure that your reports remain accurate.

Moreover, we’re committed to the success and adoption of our solutions. We regularly measure and monitor their usage because we believe in delivering not just solutions, but value that is actively utilized and drives results. Our goal is to ensure that the reports we develop are not only technically sound but also widely adopted and impactful.