#BigQuery platform

[govindhtech]

Google Cloud’s BigQuery Autonomous Data To AI Platform

BigQuery automates data analysis, transformation, and insight generation using AI. AI and natural language interaction simplify difficult operations.

The fast-paced world needs data access and a real-time data activation flywheel. Artificial intelligence that integrates directly into the data environment and works with intelligent agents is emerging. These catalysts open doors and enable self-directed, rapid action, which is vital for success. This flywheel uses Google's Data & AI Cloud to activate data in real time. BigQuery has five times more organisations than the two leading cloud providers that just offer data science and data warehousing solutions due to this emphasis.

Examples of top companies:

With BigQuery, Radisson Hotel Group enhanced campaign productivity by 50% and revenue by over 20% by fine-tuning the Gemini model.

By connecting over 170 data sources with BigQuery, Gordon Food Service established a scalable, modern, AI-ready data architecture. This improved real-time response to critical business demands, enabled complete analytics, boosted client usage of their ordering systems, and offered staff rapid insights while cutting costs and boosting market share.

J.B. Hunt is revolutionising logistics for shippers and carriers by integrating Databricks into BigQuery.

General Mills saves over $100 million using BigQuery and Vertex AI to give workers secure access to LLMs for structured and unstructured data searches.

Google Cloud is unveiling many new features with its autonomous data to AI platform powered by BigQuery and Looker, a unified, trustworthy, and conversational BI platform:

New assistive and agentic experiences based on your trusted data and available through BigQuery and Looker will make data scientists, data engineers, analysts, and business users' jobs simpler and faster.

Advanced analytics and data science acceleration: Along with seamless integration with real-time and open-source technologies, BigQuery AI-assisted notebooks improve data science workflows and BigQuery AI Query Engine provides fresh insights.

Autonomous data foundation: BigQuery can collect, manage, and orchestrate any data with its new autonomous features, which include native support for unstructured data processing and open data formats like Iceberg.

Look at each change in detail.

User-specific agents

It believes everyone should have AI. BigQuery and Looker made AI-powered helpful experiences generally available, but Google Cloud now offers specialised agents for all data chores, such as:

Data engineering agents integrated with BigQuery pipelines help create data pipelines, convert and enhance data, discover anomalies, and automate metadata development. These agents provide trustworthy data and replace time-consuming and repetitive tasks, enhancing data team productivity. Data engineers traditionally spend hours cleaning, processing, and confirming data.

The data science agent in Google's Colab notebook enables model development at every step. Scalable training, intelligent model selection, automated feature engineering, and faster iteration are possible. This agent lets data science teams focus on complex methods rather than data and infrastructure.

Looker conversational analytics lets everyone utilise natural language with data. Expanded capabilities provided with DeepMind let all users understand the agent's actions and easily resolve misconceptions by undertaking advanced analysis and explaining its logic. Looker's semantic layer boosts accuracy by two-thirds. The agent understands business language like “revenue” and “segments” and can compute metrics in real time, ensuring trustworthy, accurate, and relevant results. An API for conversational analytics is also being introduced to help developers integrate it into processes and apps.

In the BigQuery autonomous data to AI platform, Google Cloud introduced the BigQuery knowledge engine to power assistive and agentic experiences. It models data associations, suggests business vocabulary words, and creates metadata instantaneously using Gemini's table descriptions, query histories, and schema connections. This knowledge engine grounds AI and agents in business context, enabling semantic search across BigQuery and AI-powered data insights.

All customers may access Gemini-powered agentic and assistive experiences in BigQuery and Looker without add-ons in the existing price model tiers!

Accelerating data science and advanced analytics

BigQuery autonomous data to AI platform is revolutionising data science and analytics by enabling new AI-driven data science experiences and engines to manage complex data and provide real-time analytics.

First, AI improves BigQuery notebooks. It adds intelligent SQL cells to your notebook that can merge data sources, comprehend data context, and make code-writing suggestions. It also uses native exploratory analysis and visualisation capabilities for data exploration and peer collaboration. Data scientists can also schedule analyses and update insights. Google Cloud also lets you construct laptop-driven, dynamic, user-friendly, interactive data apps to share insights across the organisation.

This enhanced notebook experience is complemented by the BigQuery AI query engine for AI-driven analytics. This engine lets data scientists easily manage organised and unstructured data and add real-world context—not simply retrieve it. BigQuery AI co-processes SQL and Gemini, adding runtime verbal comprehension, reasoning skills, and real-world knowledge. Their new engine processes unstructured photographs and matches them to your product catalogue. This engine supports several use cases, including model enhancement, sophisticated segmentation, and new insights.

Additionally, it provides users with the most cloud-optimized open-source environment. Google Cloud for Apache Kafka enables real-time data pipelines for event sourcing, model scoring, communications, and analytics in BigQuery for serverless Apache Spark execution. Customers have almost doubled their serverless Spark use in the last year, and Google Cloud has upgraded this engine to handle data 2.7 times faster.

BigQuery lets data scientists utilise SQL, Spark, or foundation models on Google's serverless and scalable architecture to innovate faster without the challenges of traditional infrastructure.

An independent data foundation throughout data lifetime

An independent data foundation created for modern data complexity supports its advanced analytics engines and specialised agents. BigQuery is transforming the environment by making unstructured data first-class citizens. New platform features, such as orchestration for a variety of data workloads, autonomous and invisible governance, and open formats for flexibility, ensure that your data is always ready for data science or artificial intelligence issues. It does this while giving the best cost and decreasing operational overhead.

For many companies, unstructured data is their biggest untapped potential. Even while structured data provides analytical avenues, unique ideas in text, audio, video, and photographs are often underutilised and discovered in siloed systems. BigQuery instantly tackles this issue by making unstructured data a first-class citizen using multimodal tables (preview), which integrate structured data with rich, complex data types for unified querying and storage.

Google Cloud's expanded BigQuery governance enables data stewards and professionals a single perspective to manage discovery, classification, curation, quality, usage, and sharing, including automatic cataloguing and metadata production, to efficiently manage this large data estate. BigQuery continuous queries use SQL to analyse and act on streaming data regardless of format, ensuring timely insights from all your data streams.

Customers utilise Google's AI models in BigQuery for multimodal analysis 16 times more than last year, driven by advanced support for structured and unstructured multimodal data. BigQuery with Vertex AI are 8–16 times cheaper than independent data warehouse and AI solutions.

Google Cloud maintains open ecology. BigQuery tables for Apache Iceberg combine BigQuery's performance and integrated capabilities with the flexibility of an open data lakehouse to link Iceberg data to SQL, Spark, AI, and third-party engines in an open and interoperable fashion. This service provides adaptive and autonomous table management, high-performance streaming, auto-AI-generated insights, practically infinite serverless scalability, and improved governance. Cloud storage enables fail-safe features and centralised fine-grained access control management in their managed solution.

Finaly, AI platform autonomous data optimises. Scaling resources, managing workloads, and ensuring cost-effectiveness are its competencies. The new BigQuery spend commit unifies spending throughout BigQuery platform and allows flexibility in shifting spend across streaming, governance, data processing engines, and more, making purchase easier.

Start your data and AI adventure with BigQuery data migration. Google Cloud wants to know how you innovate with data.

[uegub]

Why Tableau is Essential in Data Science: Transforming Raw Data into Insights

Data science is all about turning raw data into valuable insights. But numbers and statistics alone don’t tell the full story—they need to be visualized to make sense. That’s where Tableau comes in.

Tableau is a powerful tool that helps data scientists, analysts, and businesses see and understand data better. It simplifies complex datasets, making them interactive and easy to interpret. But with so many tools available, why is Tableau a must-have for data science? Let’s explore.

1. The Importance of Data Visualization in Data Science

Imagine you’re working with millions of data points from customer purchases, social media interactions, or financial transactions. Analyzing raw numbers manually would be overwhelming.

That’s why visualization is crucial in data science:

Identifies trends and patterns – Instead of sifting through spreadsheets, you can quickly spot trends in a visual format.

Makes complex data understandable – Graphs, heatmaps, and dashboards simplify the interpretation of large datasets.

Enhances decision-making – Stakeholders can easily grasp insights and make data-driven decisions faster.

Saves time and effort – Instead of writing lengthy reports, an interactive dashboard tells the story in seconds.

Without tools like Tableau, data science would be limited to experts who can code and run statistical models. With Tableau, insights become accessible to everyone—from data scientists to business executives.

2. Why Tableau Stands Out in Data Science

A. User-Friendly and Requires No Coding

One of the biggest advantages of Tableau is its drag-and-drop interface. Unlike Python or R, which require programming skills, Tableau allows users to create visualizations without writing a single line of code.

Even if you’re a beginner, you can:

✅ Upload data from multiple sources

✅ Create interactive dashboards in minutes

✅ Share insights with teams easily

This no-code approach makes Tableau ideal for both technical and non-technical professionals in data science.

B. Handles Large Datasets Efficiently

Data scientists often work with massive datasets—whether it’s financial transactions, customer behavior, or healthcare records. Traditional tools like Excel struggle with large volumes of data.

Tableau, on the other hand:

Can process millions of rows without slowing down

Optimizes performance using advanced data engine technology

Supports real-time data streaming for up-to-date analysis

This makes it a go-to tool for businesses that need fast, data-driven insights.

C. Connects with Multiple Data Sources

A major challenge in data science is bringing together data from different platforms. Tableau seamlessly integrates with a variety of sources, including:

Databases: MySQL, PostgreSQL, Microsoft SQL Server

Cloud platforms: AWS, Google BigQuery, Snowflake

Spreadsheets and APIs: Excel, Google Sheets, web-based data sources

This flexibility allows data scientists to combine datasets from multiple sources without needing complex SQL queries or scripts.

D. Real-Time Data Analysis

Industries like finance, healthcare, and e-commerce rely on real-time data to make quick decisions. Tableau’s live data connection allows users to:

Track stock market trends as they happen

Monitor website traffic and customer interactions in real time

Detect fraudulent transactions instantly

Instead of waiting for reports to be generated manually, Tableau delivers insights as events unfold.

E. Advanced Analytics Without Complexity

While Tableau is known for its visualizations, it also supports advanced analytics. You can:

Forecast trends based on historical data

Perform clustering and segmentation to identify patterns

Integrate with Python and R for machine learning and predictive modeling

This means data scientists can combine deep analytics with intuitive visualization, making Tableau a versatile tool.

3. How Tableau Helps Data Scientists in Real Life

Tableau has been adopted by the majority of industries to make data science more impactful and accessible. This is applied in the following real-life scenarios:

A. Analytics for Health Care

Tableau is deployed by hospitals and research institutions for the following purposes:

Monitor patient recovery rates and predict outbreaks of diseases

Analyze hospital occupancy and resource allocation

Identify trends in patient demographics and treatment results

B. Finance and Banking

Banks and investment firms rely on Tableau for the following purposes:

✅ Detect fraud by analyzing transaction patterns

✅ Track stock market fluctuations and make informed investment decisions

✅ Assess credit risk and loan performance

C. Marketing and Customer Insights

Companies use Tableau to:

✅ Track customer buying behavior and personalize recommendations

✅ Analyze social media engagement and campaign effectiveness

✅ Optimize ad spend by identifying high-performing channels

D. Retail and Supply Chain Management

Retailers leverage Tableau to:

✅ Forecast product demand and adjust inventory levels

✅ Identify regional sales trends and adjust marketing strategies

✅ Optimize supply chain logistics and reduce delivery delays

These applications show why Tableau is a must-have for data-driven decision-making.

4. Tableau vs. Other Data Visualization Tools

There are many visualization tools available, but Tableau consistently ranks as one of the best. Here’s why:

Tableau vs. Excel – Excel struggles with big data and lacks interactivity; Tableau handles large datasets effortlessly.

Tableau vs. Power BI – Power BI is great for Microsoft users, but Tableau offers more flexibility across different data sources.

Tableau vs. Python (Matplotlib, Seaborn) – Python libraries require coding skills, while Tableau simplifies visualization for all users.

This makes Tableau the go-to tool for both beginners and experienced professionals in data science.

5. Conclusion

Tableau has become an essential tool in data science because it simplifies data visualization, handles large datasets, and integrates seamlessly with various data sources. It enables professionals to analyze, interpret, and present data interactively, making insights accessible to everyone—from data scientists to business leaders.

If you’re looking to build a strong foundation in data science, learning Tableau is a smart career move. Many data science courses now include Tableau as a key skill, as companies increasingly demand professionals who can transform raw data into meaningful insights.

In a world where data is the driving force behind decision-making, Tableau ensures that the insights you uncover are not just accurate—but also clear, impactful, and easy to act upon.

[raziakhatoon]

 Data Engineering Concepts, Tools, and Projects

All the associations in the world have large amounts of data. If not worked upon and anatomized, this data does not amount to anything. Data masterminds are the ones. who make this data pure for consideration. Data Engineering can nominate the process of developing, operating, and maintaining software systems that collect, dissect, and store the association’s data. In modern data analytics, data masterminds produce data channels, which are the structure armature.

How to become a data engineer:

 While there is no specific degree requirement for data engineering, a bachelor's or master's degree in computer science, software engineering, information systems, or a related field can provide a solid foundation. Courses in databases, programming, data structures, algorithms, and statistics are particularly beneficial. Data engineers should have strong programming skills. Focus on languages commonly used in data engineering, such as Python, SQL, and Scala. Learn the basics of data manipulation, scripting, and querying databases.

 Familiarize yourself with various database systems like MySQL, PostgreSQL, and NoSQL databases such as MongoDB or Apache Cassandra.Knowledge of data warehousing concepts, including schema design, indexing, and optimization techniques.

Data engineering tools recommendations:

    Data Engineering makes sure to use a variety of languages and tools to negotiate its objects. These tools allow data masterminds to apply tasks like creating channels and algorithms in a much easier as well as effective manner.

1. Amazon Redshift: A widely used cloud data warehouse built by Amazon, Redshift is the go-to choice for many teams and businesses. It is a comprehensive tool that enables the setup and scaling of data warehouses, making it incredibly easy to use.

One of the most popular tools used for businesses purpose is Amazon Redshift, which provides a powerful platform for managing large amounts of data. It allows users to quickly analyze complex datasets, build models that can be used for predictive analytics, and create visualizations that make it easier to interpret results. With its scalability and flexibility, Amazon Redshift has become one of the go-to solutions when it comes to data engineering tasks.

2. Big Query: Just like Redshift, Big Query is a cloud data warehouse fully managed by Google. It's especially favored by companies that have experience with the Google Cloud Platform. BigQuery not only can scale but also has robust machine learning features that make data analysis much easier. 3. Tableau: A powerful BI tool, Tableau is the second most popular one from our survey. It helps extract and gather data stored in multiple locations and comes with an intuitive drag-and-drop interface. Tableau makes data across departments readily available for data engineers and managers to create useful dashboards. 4. Looker:  An essential BI software, Looker helps visualize data more effectively. Unlike traditional BI tools, Looker has developed a LookML layer, which is a language for explaining data, aggregates, calculations, and relationships in a SQL database. A spectacle is a newly-released tool that assists in deploying the LookML layer, ensuring non-technical personnel have a much simpler time when utilizing company data.

5. Apache Spark: An open-source unified analytics engine, Apache Spark is excellent for processing large data sets. It also offers great distribution and runs easily alongside other distributed computing programs, making it essential for data mining and machine learning. 6. Airflow: With Airflow, programming, and scheduling can be done quickly and accurately, and users can keep an eye on it through the built-in UI. It is the most used workflow solution, as 25% of data teams reported using it. 7. Apache Hive: Another data warehouse project on Apache Hadoop, Hive simplifies data queries and analysis with its SQL-like interface. This language enables MapReduce tasks to be executed on Hadoop and is mainly used for data summarization, analysis, and query. 8. Segment: An efficient and comprehensive tool, Segment assists in collecting and using data from digital properties. It transforms, sends, and archives customer data, and also makes the entire process much more manageable. 9. Snowflake: This cloud data warehouse has become very popular lately due to its capabilities in storing and computing data. Snowflake’s unique shared data architecture allows for a wide range of applications, making it an ideal choice for large-scale data storage, data engineering, and data science. 10. DBT: A command-line tool that uses SQL to transform data, DBT is the perfect choice for data engineers and analysts. DBT streamlines the entire transformation process and is highly praised by many data engineers.

Data Engineering  Projects:

Data engineering is an important process for businesses to understand and utilize to gain insights from their data. It involves designing, constructing, maintaining, and troubleshooting databases to ensure they are running optimally. There are many tools available for data engineers to use in their work such as My SQL, SQL server, oracle RDBMS, Open Refine, TRIFACTA, Data Ladder, Keras, Watson, TensorFlow, etc. Each tool has its strengths and weaknesses so it’s important to research each one thoroughly before making recommendations about which ones should be used for specific tasks or projects.

  Smart IoT Infrastructure:

As the IoT continues to develop, the measure of data consumed with high haste is growing at an intimidating rate. It creates challenges for companies regarding storehouses, analysis, and visualization. 

  Data Ingestion:

Data ingestion is moving data from one or further sources to a target point for further preparation and analysis. This target point is generally a data storehouse, a unique database designed for effective reporting.

 Data Quality and Testing: 

Understand the importance of data quality and testing in data engineering projects. Learn about techniques and tools to ensure data accuracy and consistency.

 Streaming Data:

Familiarize yourself with real-time data processing and streaming frameworks like Apache Kafka and Apache Flink. Develop your problem-solving skills through practical exercises and challenges.

Conclusion:

Data engineers are using these tools for building data systems. My SQL, SQL server and Oracle RDBMS involve collecting, storing, managing, transforming, and analyzing large amounts of data to gain insights. Data engineers are responsible for designing efficient solutions that can handle high volumes of data while ensuring accuracy and reliability. They use a variety of technologies including databases, programming languages, machine learning algorithms, and more to create powerful applications that help businesses make better decisions based on their collected data.

[aamaltechnologysolutions]

How to Make AI: A Guide to An AI Developer’s Tech Stack

Globally, artificial intelligence (AI) is revolutionizing a wide range of industries, including healthcare and finance. Knowing the appropriate tools and technologies is crucial if you want to get into AI development. A well-organized tech stack can make all the difference, regardless of your level of experience as a developer. The top IT services in Qatar can assist you in successfully navigating AI development if you require professional advice.

Knowing the Tech Stack for AI Development

Programming languages, frameworks, cloud services, and hardware resources are all necessary for AI development. Let's examine the key elements of a tech stack used by an AI developer. 1. Programming Languages for the Development of AI

The first step in developing AI is selecting the appropriate programming language. Among the languages that are most frequently used are:

Because of its many libraries, including TensorFlow, PyTorch, and Scikit-Learn, Python is the most widely used language for artificial intelligence (AI) and machine learning (ML). • R: Perfect for data analysis and statistical computing. • Java: Used in big data solutions and enterprise AI applications. • C++: Suggested for AI-powered gaming apps and high-performance computing. Integrating web design services with AI algorithms can improve automation and user experience when creating AI-powered web applications.

2. Frameworks for AI and Machine Learning

  AI/ML frameworks offer pre-built features and resources to speed up development. Among the most widely utilized frameworks are: • TensorFlow: Google's open-source deep learning application library. • PyTorch: A versatile deep learning framework that researchers prefer. • Scikit-Learn: Perfect for conventional machine learning tasks such as regression and classification.

Keras is a high-level TensorFlow-based neural network API. Making the most of these frameworks is ensured by utilizing AI/ML software development expertise in order to stay ahead of AI innovation.

3. Tools for Data Processing and Management Large datasets are necessary for AI model training and optimization. Pandas, a robust Python data manipulation library, is one of the most important tools for handling and processing AI data. • Apache Spark: A distributed computing platform designed to manage large datasets. • Google BigQuery: An online tool for organizing and evaluating sizable datasets. Hadoop is an open-source framework for processing large amounts of data and storing data in a distributed manner. To guarantee flawless performance, AI developers must incorporate powerful data processing capabilities, which are frequently offered by the top IT services in Qatar.

  4. AI Development Cloud Platforms

Because it offers scalable resources and computational power, cloud computing is essential to the development of AI. Among the well-known cloud platforms are Google Cloud AI, which provides AI development tools, AutoML, and pre-trained models. • Microsoft Azure AI: This platform offers AI-driven automation, cognitive APIs, and machine learning services. • Amazon Web Services (AWS) AI: Offers computing resources, AI-powered APIs, and deep learning AMIs. Integrating cloud services with web design services facilitates the smooth deployment and upkeep of AI-powered web applications.

5. AI Hardware and Infrastructure

The development of AI demands a lot of processing power. Important pieces of hardware consist of: • GPUs (Graphics Processing Units): Crucial for AI training and deep learning. • Tensor Processing Units (TPUs): Google's hardware accelerators designed specifically for AI. • Edge Computing Devices: These are used to install AI models on mobile and Internet of Things devices.

To maximize hardware utilization, companies looking to implement AI should think about hiring professionals to develop AI/ML software.

  Top Techniques for AI Development

  1. Choosing the Appropriate AI Model Depending on the needs of your project, select between supervised, unsupervised, and reinforcement learning models.

2. Preprocessing and Augmenting Data

To decrease bias and increase model accuracy, clean and normalize the data.

3. Constant Model Training and Improvement

For improved performance, AI models should be updated frequently with fresh data.

4. Ensuring Ethical AI Procedures

To avoid prejudice, maintain openness, and advance justice, abide by AI ethics guidelines.

  In conclusion

A strong tech stack, comprising cloud services, ML frameworks, programming languages, and hardware resources, is necessary for AI development. Working with the top IT services in Qatar can give you the know-how required to create and implement AI solutions successfully, regardless of whether you're a business or an individual developer wishing to use AI. Furthermore, combining AI capabilities with web design services can improve automation, productivity, and user experience. Custom AI solutions and AI/ML software development are our areas of expertise at Aamal Technology Solutions. Get in touch with us right now to find out how AI can transform your company!

[dataplatr-1]

How Can Cloud Data Warehouse Services Drive Business Intelligence?

Traditional on-premise data systems often fall when it comes to agility, scalability, and performance. Cloud data warehouse solutions provide a modern foundation for business intelligence (BI) by offering real-time access to structured and semi-structured data, enabling faster and more accurate reporting.

What Challenges Do Legacy Data Warehouses Pose to Modern BI?

Outdated, on-prem systems can’t keep up with today’s data velocity, leaving analysts waiting for slow batch loads and siloed reports. Maintenance overhead drains IT budgets, while scaling hardware for growing workloads is costly and time-consuming. These pain points stall the very business intelligence (BI) initiatives meant to give you a competitive edge—making a shift to cloud data warehouse services the logical next step.

How Do Cloud Data Warehouse Services Enhance Data Agility and Scalability?

Storage and compute are separated in the cloud, Near-infinite concurrency supports of BI dashboards without queries. This agility enables real-time experimentation, faster decision cycles, and rapid deployment of new analytics use cases, all powered by cloud data warehouse services such as Snowflake, or BigQuery.

Why Is Data Warehouse Consulting Critical for a Successful BI Strategy?

Choosing the right architecture, migration path, and data-modeling approach isn’t trivial. Expert data warehouse consulting guides you through platform selection, cost optimization, and governance frameworks that fit regulatory needs. Consultants also build automated data pipelines and implement security best practices, ensuring your BI environment is not only high-performing but also compliant and future-proof.

How Can Cloud Data Warehouse Consulting Services Deliver Real-Time, AI-Ready Insights?

Cloud data warehouse consulting services extend beyond migration, they help in clustering, partitioning, and materialized views. By integrating streaming ingestion and machine-learning capabilities directly in the warehouse, they equip business users with predictive dashboards and AI-driven recommendations that update continuously as new data lands.

Why Dataplatr Is the Right Cloud Data Partner

At Dataplatr, we specialize in building modern, secure, and scalable cloud data platforms tailored to your business goals. Our cloud data warehouse consulting services include:

Architecture design and implementation

Migration from legacy systems

Custom ETL and data pipeline development

Integration with your analytics tools and platforms

Whether you're exploring cloud data warehouse services for the first time or looking to optimize an existing system, we’re here to help transform your data into business intelligence.

[cybersecurityict]

Cloud Database and DBaaS Market in the United States entering an era of unstoppable scalability

Cloud Database And DBaaS Market was valued at USD 17.51 billion in 2023 and is expected to reach USD 77.65 billion by 2032, growing at a CAGR of 18.07% from 2024-2032. 

Cloud Database and DBaaS Market is experiencing robust expansion as enterprises prioritize scalability, real-time access, and cost-efficiency in data management. Organizations across industries are shifting from traditional databases to cloud-native environments to streamline operations and enhance agility, creating substantial growth opportunities for vendors in the USA and beyond.

U.S. Market Sees High Demand for Scalable, Secure Cloud Database Solutions

Cloud Database and DBaaS Market continues to evolve with increasing demand for managed services, driven by the proliferation of data-intensive applications, remote work trends, and the need for zero-downtime infrastructures. As digital transformation accelerates, businesses are choosing DBaaS platforms for seamless deployment, integrated security, and faster time to market.

Get Sample Copy of This Report: https://www.snsinsider.com/sample-request/6586  

Market Keyplayers:

Google LLC (Cloud SQL, BigQuery)

Nutanix (Era, Nutanix Database Service)

Oracle Corporation (Autonomous Database, Exadata Cloud Service)

IBM Corporation (Db2 on Cloud, Cloudant)

SAP SE (HANA Cloud, Data Intelligence)

Amazon Web Services, Inc. (RDS, Aurora)

Alibaba Cloud (ApsaraDB for RDS, ApsaraDB for MongoDB)

MongoDB, Inc. (Atlas, Enterprise Advanced)

Microsoft Corporation (Azure SQL Database, Cosmos DB)

Teradata (VantageCloud, ClearScape Analytics)

Ninox (Cloud Database, App Builder)

DataStax (Astra DB, Enterprise)

EnterpriseDB Corporation (Postgres Cloud Database, BigAnimal)

Rackspace Technology, Inc. (Managed Database Services, Cloud Databases for MySQL)

DigitalOcean, Inc. (Managed Databases, App Platform)

IDEMIA (IDway Cloud Services, Digital Identity Platform)

NEC Corporation (Cloud IaaS, the WISE Data Platform)

Thales Group (CipherTrust Cloud Key Manager, Data Protection on Demand)

Market Analysis

The Cloud Database and DBaaS Market is being shaped by rising enterprise adoption of hybrid and multi-cloud strategies, growing volumes of unstructured data, and the rising need for flexible storage models. The shift toward as-a-service platforms enables organizations to offload infrastructure management while maintaining high availability and disaster recovery capabilities.

Key players in the U.S. are focusing on vertical-specific offerings and tighter integrations with AI/ML tools to remain competitive. In parallel, European markets are adopting DBaaS solutions with a strong emphasis on data residency, GDPR compliance, and open-source compatibility.

Market Trends

Growing adoption of NoSQL and multi-model databases for unstructured data

Integration with AI and analytics platforms for enhanced decision-making

Surge in demand for Kubernetes-native databases and serverless DBaaS

Heightened focus on security, encryption, and data governance

Open-source DBaaS gaining traction for cost control and flexibility

Vendor competition intensifying with new pricing and performance models

Rise in usage across fintech, healthcare, and e-commerce verticals

Market Scope

The Cloud Database and DBaaS Market offers broad utility across organizations seeking flexibility, resilience, and performance in data infrastructure. From real-time applications to large-scale analytics, the scope of adoption is wide and growing.

Simplified provisioning and automated scaling

Cross-region replication and backup

High-availability architecture with minimal downtime

Customizable storage and compute configurations

Built-in compliance with regional data laws

Suitable for startups to large enterprises

Forecast Outlook

The market is poised for strong and sustained growth as enterprises increasingly value agility, automation, and intelligent data management. Continued investment in cloud-native applications and data-intensive use cases like AI, IoT, and real-time analytics will drive broader DBaaS adoption. Both U.S. and European markets are expected to lead in innovation, with enhanced support for multicloud deployments and industry-specific use cases pushing the market forward.

Access Complete Report: https://www.snsinsider.com/reports/cloud-database-and-dbaas-market-6586 

Conclusion

The future of enterprise data lies in the cloud, and the Cloud Database and DBaaS Market is at the heart of this transformation. As organizations demand faster, smarter, and more secure ways to manage data, DBaaS is becoming a strategic enabler of digital success. With the convergence of scalability, automation, and compliance, the market promises exciting opportunities for providers and unmatched value for businesses navigating a data-driven world.

Related reports:

U.S.A leads the surge in advanced IoT Integration Market innovations across industries

U.S.A drives secure online authentication across the Certificate Authority Market

U.S.A drives innovation with rapid adoption of graph database technologies

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.

Contact Us:

Jagney Dave - Vice President of Client Engagement

Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)

Mail us: info@snsinsider.com

[govindhtech]

Aible And Google Cloud: Gen AI Models Sets Business Security

Enterprise controls and generative AI for business users in real time.

Aible

With solutions for customer acquisition, churn avoidance, demand prediction, preventive maintenance, and more, Aible is a pioneer in producing business impact from AI in less than 30 days. Teams can use AI to extract company value from raw enterprise data. Previously using BigQuery’s serverless architecture to save analytics costs, Aible is now working with Google Cloud to provide users the confidence and security to create, train, and implement generative AI models on their own data.

The following important factors have surfaced as market awareness of generative AI’s potential grows:

Enabling enterprise-grade control

Businesses want to utilize their corporate data to allow new AI experiences, but they also want to make sure they have control over their data to prevent unintentional usage of it to train AI models.

Reducing and preventing hallucinations

The possibility that models may produce illogical or non-factual information is another particular danger associated with general artificial intelligence.

Empowering business users

Enabling and empowering business people to utilize gen AI models with the least amount of hassle is one of the most beneficial use cases, even if gen AI supports many enterprise use cases.

Scaling use cases for gen AI

Businesses need a method for gathering and implementing their most promising use cases at scale, as well as for establishing standardized best practices and controls.

Regarding data privacy, policy, and regulatory compliance, the majority of enterprises have a low risk tolerance. However, given its potential to drive change, they do not see postponing the deployment of Gen AI as a feasible solution to market and competitive challenges. As a consequence, Aible sought an AI strategy that would protect client data while enabling a broad range of corporate users to swiftly adapt to a fast changing environment.

In order to provide clients complete control over how their data is used and accessed while creating, training, or optimizing AI models, Aible chose to utilize Vertex AI, Google Cloud’s AI platform.

Enabling enterprise-grade controls 

Because of Google Cloud’s design methodology, users don’t need to take any more steps to ensure that their data is safe from day one. Google Cloud tenant projects immediately benefit from security and privacy thanks to Google AI products and services. For example, protected customer data in Cloud Storage may be accessed and used by Vertex AI Agent Builder, Enterprise Search, and Conversation AI. Customer-managed encryption keys (CMEK) can be used to further safeguard this data.

With Aible‘s Infrastructure as Code methodology, you can quickly incorporate all of Google Cloud’s advantages into your own applications. Whether you choose open models like LLama or Gemma, third-party models like Anthropic and Cohere, or Google gen AI models like Gemini, the whole experience is fully protected in the Vertex AI Model Garden.

In order to create a system that may activate third-party gen AI models without disclosing private data outside of Google Cloud, Aible additionally collaborated with its client advisory council, which consists of Fortune 100 organizations. Aible merely transmits high-level statistics on clusters which may be hidden if necessary instead of raw data to an external model. For instance, rather of transmitting raw sales data, it may communicate counts and averages depending on product or area.

This makes use of k-anonymity, a privacy approach that protects data privacy by never disclosing information about groups of people smaller than k. You may alter the default value of k; the more private the information transmission, the higher the k value. Aible makes the data transmission even more secure by changing the names of variables like “Country” to “Variable A” and values like “Italy” to “Value X” when masking is used.

Mitigating hallucination risk

It’s crucial to use grounding, retrieval augmented generation (RAG), and other strategies to lessen and lower the likelihood of hallucinations while employing gen AI. Aible, a partner of Built with Google Cloud AI, offers automated analysis to support human-in-the-loop review procedures, giving human specialists the right tools that can outperform manual labor.

Using its auto-generated Information Model (IM), an explainable AI that verifies facts based on the context contained in your structured corporate data at scale and double checks gen AI replies to avoid making incorrect conclusions, is one of the main ways Aible helps eliminate hallucinations.

Hallucinations are addressed in two ways by Aible’s Information Model:

It has been shown that the IM helps lessen hallucinations by grounding gen AI models on a relevant subset of data.

To verify each fact, Aible parses through the outputs of Gen AI and compares them to millions of responses that the Information Model already knows.

This is comparable to Google Cloud’s Vertex AI grounding features, which let you link models to dependable information sources, like as your company’s papers or the Internet, to base replies in certain data sources. A fact that has been automatically verified is shown in blue with the words “If it’s blue, it’s true.” Additionally, you may examine a matching chart created only by the Information Model and verify a certain pattern or variable.

The graphic below illustrates how Aible and Google Cloud collaborate to provide an end-to-end serverless environment that prioritizes artificial intelligence. Aible can analyze datasets of any size since it leverages BigQuery to efficiently analyze and conduct serverless queries across millions of variable combinations. One Fortune 500 client of Aible and Google Cloud, for instance, was able to automatically analyze over 75 datasets, which included 150 million questions and answers with 100 million rows of data. That assessment only cost $80 in total.

Aible may also access Model Garden, which contains Gemini and other top open-source and third-party models, by using Vertex AI. This implies that Aible may use AI models that are not Google-generated while yet enjoying the advantages of extra security measures like masking and k-anonymity.

All of your feedback, reinforcement learning, and Low-Rank Adaptation (LoRA) data are safely stored in your Google Cloud project and are never accessed by Aible.

Read more on Govindhtech.com

[transcuratorsblog]

Top Reporting Tools Used by Digital Marketing Agencies in 2025

Data is no longer a luxury—it's the foundation of every high-performing campaign. In 2025, clients want more than just creative deliverables; they expect detailed insights, ROI breakdowns, and transparent reporting. That’s why every leading Digital Marketing Agency relies on powerful reporting tools to track performance, showcase results, and guide decision-making.

From real-time dashboards to AI-powered insights, the tools listed below are redefining how agencies visualize and share marketing success. Whether you're an in-house marketer or a business owner evaluating your agency’s performance, these are the platforms worth knowing.

1. Google Looker Studio (formerly Data Studio)

Still a favorite in 2025, Looker Studio remains one of the most accessible and flexible reporting tools available. It integrates seamlessly with Google Analytics, Google Ads, Search Console, and BigQuery.

Why agencies love it:

Real-time dashboards

Custom visuals and filters

Easy sharing with clients

Free with unlimited reports

It’s the go-to for clear, interactive dashboards that can be tailored to each client’s KPIs.

2. AgencyAnalytics

Built specifically for agencies, this all-in-one platform supports 75+ integrations including SEO, PPC, email, and social media.

Top features:

White-label dashboards and PDF reports

Automated weekly/monthly reporting

Client login portals

SEO and backlink tracking

AgencyAnalytics saves hours of manual data pulling while offering a polished, client-friendly interface.

3. Databox

Databox is known for its mobile-friendly reports and sleek dashboards. It pulls data from over 100 sources including HubSpot, Facebook Ads, Shopify, and Google Analytics.

Highlights:

Real-time performance alerts

Goal tracking with daily updates

Predictive metrics powered by AI

Visual storytelling with drag-and-drop design

Perfect for agencies managing cross-platform campaigns that need daily health checks and performance snapshots.

4. DashThis

DashThis focuses on simplicity and automation. Designed for marketing teams, it offers integration with Google Ads, Bing Ads, LinkedIn, Facebook, Mailchimp, and more.

Why it stands out:

Templates for common use cases

Unlimited users per dashboard

Scheduled email reports

Cost-effective for small to mid-sized agencies

Agencies love how quickly they can build and clone reports across multiple client accounts.

5. Whatagraph

Whatagraph turns complex data into colorful, digestible visuals—great for clients who aren’t data-savvy.

Features include:

Pre-built cross-channel templates

Multi-client management

KPI comparisons over time

Email and Slack report delivery

Its design-first interface is especially useful for monthly performance summaries and campaign wrap-ups.

6. Supermetrics

While not a dashboard in itself, Supermetrics is a data pipeline tool that pulls marketing data into Google Sheets, Excel, Looker Studio, or BigQuery.

Why agencies use it:

Pulls granular campaign data automatically

Enables custom report building

Supports Facebook, TikTok, Google Ads, HubSpot, and more

Ideal for analysts who want full control over metrics

It’s a favorite for agencies with data teams who build customized reporting systems internally.

7. ReportGarden

Designed for PPC and SEO-focused agencies, ReportGarden excels at creating detailed campaign performance reports.

Key features:

Invoice and proposal builder

Budget pacing insights

Cross-client reporting

Google Ads, Facebook Ads, LinkedIn integrations

Its financial management tools also make it popular among agencies that handle billing and performance reporting together.

8. Klipfolio

Klipfolio offers advanced data visualization capabilities for agencies working with complex data sets or enterprise clients.

Strengths include:

300+ data connectors

Embedded analytics

Custom formulas and scripting

Real-time performance alerts

It’s ideal for tech-savvy teams who want to build sophisticated, dynamic dashboards with deep customization.

9. HubSpot Marketing Hub Reporting

For agencies managing inbound campaigns or CRM-based automation, HubSpot’s reporting dashboard offers excellent insight into the full buyer journey.

Useful for:

Tracking lifecycle stages

Email and lead nurturing performance

Sales and marketing attribution

Funnel drop-off analysis

It’s an all-in-one solution for agencies offering lead gen, email automation, and CRM management.

10. Oviond

Oviond is a relatively new but fast-growing reporting tool in 2025. It combines simplicity with robust functionality.

Why it’s gaining traction:

Beautiful client-facing dashboards

Supports 100+ marketing integrations

Smart reporting summaries with insights

Flexible plans for startups to large agencies

Its user-friendly UI makes it easy for account managers to build and deliver insightful reports without technical skills.

Final Thoughts: Choose Tools That Work for You and Your Clients

No single reporting platform fits all, but every top-tier Digital Marketing Agency in 2025 uses a smart blend of tools that align with their workflow, client goals, and reporting frequency. From real-time analytics to sleek monthly summaries, great reporting is what turns raw data into actionable growth.

If your agency still relies on manual spreadsheets or static PDFs, it’s time to upgrade. With the right reporting stack, you not only deliver clarity and transparency—you prove your value with every campaign.

[vaithu]

Netflix Clone Business Model in 2025: What Every OTT Startup Should Know

In 2025, the OTT streaming market is booming more than ever. With Netflix leading the charge, entrepreneurs are increasingly eyeing Netflix clone Business platforms as a gateway to tap into this digital entertainment revolution. However, building a Netflix clone is not just about replicating design — it’s about implementing a robust business model that can scale, engage users, and monetize effectively.

The OTT Landscape in 2025

The global OTT video market is projected to exceed $400 billion by 2027, with mobile viewership, AI-based personalization, and regional content driving growth. The demand for niche platforms, ad-supported models, and interactive experiences is at an all-time high.

Netflix’s model has evolved to include:

Tiered pricing plans

Ad-supported subscriptions

Gaming content

Localized productions

These serve as foundational strategies to emulate and adapt in a Netflix clone.

Key Components of the Netflix Clone Business Model

1. Content Licensing & Originals

A successful Netflix clone needs two pillars: licensed content and original productions. In 2025:

Original content offers higher ROI and brand value.

Local and regional productions enhance reach and retention.

Tip: Form early partnerships with indie creators and studios to source unique, exclusive content.

2. Monetization Models

Netflix clones must offer flexible monetization options. Choose or combine the following based on your audience:

ModelDescriptionExample PlatformsSVOD (Subscription)Monthly or annual fee for unlimited accessNetflix, Disney+AVOD (Ad-supported)Free access supported by advertisementsTubi, YouTubeTVOD (Pay-per-view)Rent/buy specific contentApple TV, Google PlayHybridCombines free (ad) and premium (subscription) modelsHulu, Peacock

3. Tech Infrastructure

A Netflix clone must prioritize:

Scalable cloud infrastructure (AWS, Azure, GCP)

AI-driven recommendation engine

Multi-platform compatibility (Web, Android, iOS, Smart TVs)

Offline viewing & DRM protection

Don’t forget: buffer-free streaming and responsive UX are deal-breakers in 2025.

4. User Acquisition & Retention Strategy

In 2025, it’s not enough to build a platform — you need a community. Consider:

Free trial periods

Referral bonuses

Interactive elements (polls, watch parties, fan forums)

Push notifications based on watch behavior

Retention thrives when users feel recognized and entertained.

Revenue Streams to Diversify Income

Diversify beyond just subscriptions with:

Ads (AVOD): Dynamic video ads and sponsored content.

Merchandise: Branded products from popular shows.

Licensing: Sell original content to other networks.

In-app purchases: Paywalls for premium episodes or features.

Gaming content integration (like Netflix’s push into mobile games).

Analytics and AI: The Backbone of Strategy

Leverage AI-based analytics to:

Track content performance

Segment users by behavior

Predict churn

Optimize pricing

Tools like Mixpanel, Amplitude, and BigQuery are key to smart decisions.

Success Tips for Launching in 2025

Start lean: Launch an MVP targeting a specific niche (e.g., K-drama fans, fitness content).

Use white-label OTT platforms like Miracuves’ Netflix Clone solution to fast-track development.

Prioritize mobile-first UX — 70%+ of OTT content is consumed on mobile.

Stay compliant: With increasing global scrutiny, ensure GDPR, COPPA, and local regulations are met.

Experiment with AI: Dynamic pricing, personalized trailers, voice-based navigation — all powered by AI.

Final Thoughts

The Netflix clone business model in 2025 demands more than replication — it requires innovation, flexibility, and audience obsession. By adopting the right content strategy, monetization mix, and user experience enhancements, OTT startups can not only compete but thrive in this booming sector.

This is where Miracuves steps in.

With years of experience in building robust, scalable OTT platforms, Miracuves delivers custom Netflix clone solutions that are not only technically sound but also aligned with current and future market demands. From content delivery and user analytics to monetization and localization — Miracuves equips you with everything needed to lead in the OTT space.

Whether you’re a startup or an established media house, Miracuves can accelerate your OTT journey with innovation, speed, and strategic support.

👉 Contact Miracuves today to build the next big Netflix alternative.

[cymetrixsoftware]

Transforming Sales Decisions with Generative AI and BigQuery

A leading enterprise was struggling to make timely and informed sales decisions due to fragmented data sources and a lack of real-time analytics. These limitations led to delayed insights for the sales team, hampering their ability to respond swiftly to market changes and evolving customer needs. To address this, Cymetrix Software implemented a comprehensive solution by integrating Google BigQuery with Generative AI capabilities. This powerful combination enabled the consolidation of disparate data sources into a unified, scalable platform for real-time data analysis and visualization. Leveraging BigQuery’s advanced data warehousing features, the solution facilitated efficient processing of large volumes of sales data. Meanwhile, Generative AI models were used to uncover predictive insights, track sales trends, and provide actionable recommendations through conversational analytics. Additionally, Cymetrix developed interactive dashboards, empowering the sales team with real-time visibility into key performance metrics and enabling them to make faster, data-driven decisions. As a result, the organization witnessed significant improvements in decision-making speed and accuracy, reduced manual reporting through automation, and enhanced overall sales performance optimization. Predictive analytics further helped identify high-potential leads and streamline sales strategies, ultimately contributing to increased revenue. For a more detailed understanding, you can read the full case study here: Transforming Sales Decisions with Generative AI and BigQuery.

[transcuratorsblog]

Top PPC Reporting Tools to Simplify Client Reporting

Running successful PPC campaigns is only half the battle; the other half lies in clear, consistent, and insightful reporting. For digital marketers and agencies offering pay per click services, client reporting is a critical part of demonstrating ROI and justifying ad spend. However, manually pulling data from platforms like Google Ads, Facebook Ads, LinkedIn Ads, or Microsoft Ads can be time-consuming and error-prone. That’s where PPC reporting tools come in.

These tools help automate the reporting process, offer data visualization, and provide customizable dashboards that keep clients informed and engaged. In this article, we’ll explore the top PPC reporting tools that simplify client reporting and help agencies deliver results more effectively.

1. Google Looker Studio (formerly Data Studio)

Best for: Free, customizable reports using Google products

Google Looker Studio is one of the most popular and cost-effective reporting tools for PPC. It integrates seamlessly with Google Ads, Google Analytics, Search Console, and BigQuery, allowing you to build interactive, real-time dashboards.

Key Features:

Drag-and-drop interface

Custom charts, graphs, and scorecards

Sharing and collaboration features

Integration with third-party connectors (Facebook Ads, Bing, etc.)

Why it’s useful: For agencies using Google’s ecosystem, Looker Studio is a powerful and budget-friendly choice that can be branded for clients.

2. Swydo

Best for: Automated, white-labeled PPC reports

Swydo is designed specifically for digital marketers offering PPC, SEO, and social media services. It supports automated report scheduling and performance monitoring across multiple ad platforms.

Key Features:

Pre-built PPC report templates

Integration with Google Ads, Facebook Ads, LinkedIn Ads, and more

Goal tracking and KPIs

White-label branding options

Why it’s useful: It saves time on recurring reports and enhances professionalism through client-specific templates and custom branding.

3. ReportGarden

Best for: Agencies managing multiple PPC accounts

ReportGarden is a comprehensive client reporting and proposal tool that supports PPC, SEO, social, and email campaign reporting.

Key Features:

Pre-built widgets for key PPC metrics

Cross-channel reporting (Google, Bing, Facebook, Instagram)

Client portal access

Proposal and invoicing features for agencies

Why it’s useful: ReportGarden not only simplifies reporting but also streamlines client communications, making it a great choice for growing agencies.

4. Supermetrics

Best for: Data aggregation for advanced reporting in Excel, Google Sheets, and Looker Studio

Supermetrics isn’t a standalone dashboard but a data connector tool that pulls PPC data into your preferred reporting environment (Sheets, Excel, or Looker Studio).

Key Features:

Connects to over 70 data sources

Automates data pulls and refreshes

Supports blending data from multiple platforms

Ideal for custom and enterprise-level reporting

Why it’s useful: It offers total flexibility for agencies with specific reporting formats or existing spreadsheets.

5. AgencyAnalytics

Best for: All-in-one agency reporting with client dashboards

AgencyAnalytics offers a clean, easy-to-navigate reporting platform tailored for digital agencies. It includes keyword tracking, SEO audits, social analytics, and PPC reporting.

Key Features:

70+ marketing integrations

Custom dashboards and scheduled reports

White-label client portals

Campaign performance alerts

Why it’s useful: Agencies can build branded portals where clients can log in to see their performance anytime, reducing back-and-forth email communication.

6. Whatagraph

Best for: Visually stunning PPC and cross-channel marketing reports

Whatagraph is known for its sleek, colorful, and intuitive reporting style. It’s ideal for marketers who want to impress clients with eye-catching visuals without sacrificing data accuracy.

Key Features:

Multi-channel reports

Drag-and-drop widgets

Scheduled report automation

Pre-built templates for quick deployment

Why it’s useful: It’s perfect for agencies focused on client experience and visual storytelling.

7. DashThis

Best for: Easy-to-use PPC dashboards with minimal setup

DashThis is a straightforward dashboard tool that lets you create PPC reports quickly with minimal effort. Its intuitive interface makes it accessible even to non-technical marketers.

Key Features:

Simple drag-and-drop editor

Supports all major PPC and analytics platforms

White-labeled reports and client sharing

30+ pre-built integrations

Why it’s useful: It’s ideal for freelancers or small agencies who want powerful reports without the complexity of larger platforms.

Conclusion: Simplify Client Success with Pay Per Click Services and Smart Reporting

As PPC campaigns grow more complex, clear reporting becomes essential—not just for transparency, but also for building long-term client trust. Choosing the right PPC reporting tool allows marketers to automate tasks, eliminate manual errors, and focus more on strategic improvements rather than number crunching.

Whether you’re managing one campaign or a hundred, these tools help deliver insights your clients can understand and appreciate. For businesses that don’t have the in-house bandwidth, turning to professional pay per click services ensures not only expert campaign management but also streamlined, results-driven reporting that supports smarter decision-making and better ROI.

[yfthg]

Top Most Effective Customer Journey Analytics Solutions in Airlines

NUMR CXM: Elevating Airline Passenger Experience with Real-Time Journey Analytics

NUMR CXM is redefining customer journey analytics in the airline industry. By mapping end-to-end passenger interactions—from flight search to post-flight feedback—NUMR enables airlines to visualize customer drop-offs, delays in service, and satisfaction triggers. Using AI-powered predictive insights and omnichannel integration, NUMR CXM helps carriers proactively address pain points, increase loyalty, and maximize revenue per traveler. Airlines leveraging NUMR’s platform benefit from smarter segmentation, real-time action triggers, and optimized NPS across the journey lifecycle.

Why Customer Journey Analytics Matters in Aviation

In the hyper-competitive airline space, customer loyalty is fragile and influenced by multiple micro-moments—from ticket booking and check-in to baggage handling and flight experience. Customer journey analytics empowers airlines to:

Pinpoint moments of friction

Track engagement across mobile, web, and airport touchpoints

Deliver personalized interventions in real time

Improve operational efficiency and CX KPIs

Top Customer Journey Analytics Solutions in Airlines (2025)

1. NUMR CXM

AI-powered journey mapping tailored to aviation

Omnichannel passenger data integration

Predictive churn and loyalty insights

Personalized NPS and real-time feedback loops

2. Adobe Experience Platform

Real-time customer profile unification

Journey orchestration across devices

Visualization of path-to-purchase in airline portals

3. Salesforce Customer 360

Travel-specific CX dashboards

AI-driven service insights for call centers and loyalty programs

Integration with airline CRM and booking systems

4. Qualtrics Experience iD

Deep integration with operational data (e.g., delay reports)

Real-time surveys at critical travel moments

Voice-of-Customer (VoC) insights to prevent churn

5. Google Analytics 4 (GA4) with BigQuery

Tracks digital touchpoints pre-booking to boarding

Combines site behavior with predictive purchase modeling

Great for low-cost carriers optimizing conversion

6. Genesys Cloud CX

Advanced contact center analytics

Tracks call/chat/email journey interruptions

Identifies emotional sentiment in service breakdowns

7. Mixpanel

Granular event tracking on airline apps

Useful for enhancing loyalty program interaction

A/B testing and funnel analysis for digital CX teams

8. Medallia Experience Cloud

Real-time survey triggers based on NPS dips

Great airport and inflight service integration

Customizable reporting for route-level CX management

9. Sprinklr Unified CXM

Social media journey insights across platforms

Tracks complaints, praises, and competitor comparison

Excellent for airline brand reputation monitoring

10. SAS Customer Intelligence 360

Predictive personalization for frequent flyer programs

Advanced segmentation of leisure vs business travelers

Supports real-time marketing engagement

Geographic Insight: Journey Analytics in Indian and Global Aviation

India’s airline sector is experiencing explosive growth with increasing digital bookings and higher CX expectations. Globally, airlines in North America and the Middle East are leading in AI-driven journey analytics adoption. NUMR CXM is well-positioned to serve both these markets with scalable, agile platforms.

FAQs – Airline Journey Analytics Solutions

What is customer journey analytics in aviation?

It refers to the process of tracking and analyzing every interaction a passenger has with an airline, across all channels and touchpoints.

How does NUMR CXM enhance passenger experience?

NUMR CXM uses AI and predictive analytics to identify friction points in real time, enabling airlines to act quickly and improve satisfaction and loyalty.

Can airlines personalize experiences using journey data?

Yes, by mapping behavior and preferences, airlines can personalize offers, services, and communication at every journey stage.

Which journey stages are most prone to dissatisfaction?

Common friction points include booking, baggage claim, delay management, and customer service interactions.

Final Takeaway

Customer journey analytics is no longer optional—it's essential for airlines striving to deliver seamless, memorable travel experiences. By leveraging platforms like NUMR CXM, aviation brands can transform every touchpoint into an opportunity for delight, loyalty, and competitive differentiation in 2025 and beyond.

[govindhtech]

Lightning Engine: A New Era for Apache Spark Speed

Apache Spark analyses enormous data sets for ETL, data science, machine learning, and more. Scaled performance and cost efficiency may be issues. Users often experience resource utilisation, data I/O, and query execution bottlenecks, which slow processing and increase infrastructure costs.

Google Cloud knows these issues well. Lightning Engine (preview), the latest and most powerful Spark engine, unleashes your lakehouse's full potential and provides best-in-class Spark performance.

Lightning Engine?

Lightning Engine prioritises file-system layer and data-access connector optimisations as well as query and execution optimisations.

Lightning Engine enhances Spark query speed by 3.6x on TPC-H workloads at 10TB compared to open source Spark on equivalent equipment.

Lightning Engine's primary advancements are shown above:

Lightning Engine's Spark optimiser is improved by Google's F1 and Procella experience. This advanced optimiser includes adaptive query execution for join removal and exchange reuse, subquery fusion to consolidate scans, advanced inferred filters for semi-join pushdowns, dynamic in-filter generation for effective row-group pruning in Iceberg and Delta tables, optimising Bloom filters based on listing call statistics, and more. Scan and shuffle savings are significant when combined.

Lightning Engine's execution engine boosts performance with a native Apache Gluten and Velox implementation designed for Google's hardware. This uses unified memory management to switch between off-heap and on-heap memory without changing Spark settings. Lightning Engine now supports operators, functions, and Spark data types and can automatically detect when to use the native engine for pushdown results.

Lightning Engine employs columnar shuffle with an optimised serializer-deserializer to decrease shuffle data.

Lightning Engine uses a parquet parser for prefetching, caching, and in-filtering to reduce data scans and metadata operations.

Lightning Engine increases BigQuery and Google Cloud Storage connection to speed up its native engine. An optimised file output committer boosts Spark application performance and reliability, while the upgraded Cloud Storage connection reduces metadata operations to save money. By providing data directly to the engine in Apache Arrow format and eliminating row-to-columnar conversions, the new native BigQuery connection simplifies data delivery.

Lightning Engine works with SQL APIs and Apache Spark DataFrame, so workloads run seamlessly without code changes.

Lightning Engine—why?

Lightning Engine outperforms cloud Spark competitors and is cheaper. Open formats like Apache Iceberg and Delta Lake can boost business efficiency using BigQuery and Google Cloud's cutting-edge AI/ML.

Lightning Engine outperforms DIY Spark implementations, saving you money and letting you focus on your business challenges.

Advantages

Main lightning engine benefits

Faster query performance: Uses a new Spark processing engine with vectorised execution, intelligent caching, and optimised storage I/O.

Leading industry price-performance ratio: Allows customers to manage more data for less money by providing superior performance and cost effectiveness.

Intelligible Lakehouse integration: Integrates with Google Cloud services including BigQuery, Vertex AI, Apache Iceberg, and Delta Lake to provide a single data analytics and AI platform.

Optimised BigQuery and Cloud Storage connections increase data access latency, throughput, and metadata operations.

Flexible deployments: Cluster-based and serverless.

Lightning Engine boosts performance, although the impact depends on workload. It works well for compute-intensive Spark Dataframe API and Spark SQL queries, not I/O-bound tasks.

Spark's Google Cloud future

Google Cloud is excited to apply Google's size, performance, and technical prowess to Apache Spark workloads with the new Lightning Engine data query engine, enabling developers worldwide. It wants to speed it up in the following months, so this is just the start!

Google Cloud Serverless for Apache Spark and Dataproc on Google Compute Engine premium tiers demonstrate Lightning Engine. Both services offer GPU support for faster machine learning and task monitoring for operational efficiency.

[shakshi09]

What’s the function of Tableau Prep?

Tableau Prep is a data preparation tool from Tableau that helps users clean, shape, and organize data before it is analyzed or visualized. It is especially useful for data analysts and business intelligence professionals who need to prepare data quickly and efficiently without writing complex code.

The core function of Tableau Prep is to simplify the data preparation process through an intuitive, visual interface. Users can drag and drop datasets, apply filters, rename fields, split or combine columns, handle null values, pivot data, and even join or union multiple data sources. These actions are displayed in a clear, step-by-step workflow, which makes it easy to understand how data is transformed at each stage.

Tableau Prep includes two main components: Prep Builder, used to create and edit data preparation workflows, and Prep Conductor, which automates the running of flows and integrates with Tableau Server or Tableau Cloud for scheduled data refreshes. This automation is a major advantage, especially in dynamic environments where data updates regularly.

Another significant benefit is real-time previews. As users manipulate data, they can instantly see the effects of their actions, allowing for better decisions and error checking. It supports connections to various data sources such as Excel, SQL databases, and cloud platforms like Google BigQuery or Amazon Redshift.

Tableau Prep’s seamless integration with Tableau Desktop means that once data is prepped, it can be directly pushed into visualization dashboards without exporting and re-importing files.

In short, Tableau Prep helps streamline the otherwise time-consuming process of cleaning and preparing data, making it more accessible to analysts without deep programming knowledge.

If you’re looking to master tools like Tableau Prep and enter the analytics field, consider enrolling in a data analyst course with placement for hands-on training and career support.