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Simon K., Python Software Engineer with data engineering skills

Hannover, Germany
Last Updated: 6 Jul 2023

- 2+ years of experience with Python as a Data Engineer and Deep/Machine Learning Intern - Experience with Data Vault modeling and AWS cloud services (S3, Lambda, and Batch) - Cloud Services: Sagemaker, Google BigQuery, Google Data Studio, MS Azure Databricks, IBM Spectrum LSF, Slurm - Data Science Frameworks: PyTorch, TensorFlow, PySpark, NumPy, SciPy, scikit-learn, Pandas, Matplotlib, NLTK, OpenCV - Proficient in SQL, Python, Linux, Git, and Bash scripting. - Had experience leading a BI development team and served as a Scrum Master. - Native English - Native German

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Oleksandr T., BI Analyst

Odesa, Ukraine
Last Updated: 20 Dec 2023

- Experienced BI Analyst with a diverse background in data analysis, data engineering, and data visualization - Proficient in utilizing various BI tools such as PowerBI, Tableau, Metabase, and Periscope for creating reports and visualizations. - Skilled in exploratory data analysis using Python/pandas or SQL, as well as data manipulation in Excel - Experienced in database engineering and ETL processes using airflow/prefect/databricks as an orchestration tool and dbt for transformations. - Knowledge of data governance and implementing data standards. - DB: Postgres, BigQuery/Snowflake. - Advanced English

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Microsoft Power BI

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Serhii K., Lead Data Science Engineer /AI & ML Engineer

Last Updated: 31 Oct 2023

- Highly experienced Head of Data Science with 12+ years of experience in creating and managing DS/ML teams in startups and corporate projects; - Proficient in AI, NLP, Adtech, Fintech, and CV; - Strong leadership skills and a client-oriented approach; - Skilled in Python, SQL, Prompt Engineering, HuggingFace, PyTorch, Scikit-learn, Pandas, LangChain, LlamaIndex, Spacy, GPT, Plotly, GCP, AWS, Azure, Postgre, MongoDB, BigQuery, and Vector DB; - Proactive in implementing innovative approaches for product features using Generative AI, LLM, and GPT; - Experienced in proposing innovative solutions for new business problems and managing teams; - Holds a PhD in Data Science and a Master's degree in Computer Science.

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NLP   6 yr.



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Alex K., Data Engineer

Oradea, Romania
Last Updated: 13 Nov 2023

- Senior Data Engineer with a strong technology core background in companies focused on data collection, management, and analysis. - Proficient in SQL, NoSQL, Python, Pyspark, Oracle PL/SQL, Microsoft T-SQL, and Perl/Bash. - Experienced in working with AWS stack (Redshift, Aurora, PostgreSQL, Lambda, S3, Glue, Terraform, CodePipeline) and GCP stack (BigQuery, Dataflow, Dataproc, Pub/Sub, Data Studio, Terraform, Cloud Build). - Skilled in working with RDBMS such as Oracle, MySQL, PostgreSQL, MsSQL, and DB2. - Familiar with Big Data technologies like AWS Redshift, GCP BigQuery, MongoDB, Apache Hadoop, AWS DynamoDB, and Neo4j. - Proficient in ETL tools such as Talend Data Integration, Informatica, Oracle Data Integrator (ODI), IBM Datastage, and Apache Airflow. - Experienced in using Git, Bitbucket, SVN, and Terraform for version control and infrastructure management. - Holds a Master's degree in Environmental Engineering and has several years of experience in the field. - Has worked on various projects as a data engineer, including operational data warehousing, data integration for crypto wallets/De-Fi, cloud data hub architecture, data lake migration, GDPR reporting, CRM migration, and legacy data warehouse migration. - Strong expertise in designing and developing ETL processes, performance tuning, troubleshooting, and providing technical consulting to business users. - Familiar with agile methodologies and has experience working in agile environments. - Has experience with Oracle, Microsoft SQL Server, and MongoDB databases. - Has worked in various industries including financial services, automotive, marketing, and gaming. - Advanced English - Available in 4 weeks after approval for the project

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Amazon Web Services (AWS)

Amazon Web Services (AWS)

Google Cloud Platform (GCP)

Google Cloud Platform (GCP)

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Borys, Data Science Engineer

Last Updated: 7 Mar 2024

Certified Data Scientist with a strong focus on NLP, CV, and Recommender Systems backed by 4 years of commercial experience. Proficient in Python with a rich toolset including Pandas, numpy, TensorFlow, and Keras. Possesses a solid track record in building products from scratch and devising innovative solutions with machine learning and data processing methodologies. Hands-on experience in deploying scalable solutions using Kubeflow, Docker, and CI/CD practices, complemented by proficiency with various databases such as MySQL and BigQuery. With a Bachelor’s and Master’s degrees in Cybersecurity Engineering, and continued education via a PhD, the engineer exemplifies a deep understanding of computer science fundamentals and data science trends. This technical expertise, combined with domain knowledge in e-commerce and network security, distinguishes the potential candidate as a valuable asset for fostering growth and innovation in technology-driven environments.

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Unified Modeling Language (UML)

Unified Modeling Language (UML)

Google Compute Engine (GCE)

Google Compute Engine (GCE)

Model-view-controller (MVC) pattern

Model-view-controller (MVC) pattern

AWS Machine learning services (ML)

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Table of Contents

Pros & cons of Google BigQuery

6 Pros of Google BigQuery

  • Scalability: Google BigQuery is highly scalable and can handle large volumes of data with ease. It allows you to process and analyze terabytes to petabytes of data without any infrastructure constraints.
  • Speed: BigQuery is known for its fast query performance. It leverages Google’s infrastructure and advanced parallel processing capabilities to quickly process and retrieve data, enabling near real-time analytics.
  • Cost-effective: With BigQuery, you only pay for the queries you run and the storage you use. It offers a flexible pricing model that allows you to control costs based on your usage patterns. Additionally, BigQuery provides cost-saving features like data compression and columnar storage.
  • Managed service: Google BigQuery is a fully managed service, which means you don’t have to worry about infrastructure management, software updates, or scaling. Google takes care of all the operational aspects, allowing you to focus on your data analysis.
  • Integration with other Google Cloud services: BigQuery seamlessly integrates with other Google Cloud services like Google Cloud Storage, Dataflow, and Dataprep. This integration enables you to easily ingest, transform, and analyze data from various sources within the Google Cloud ecosystem.
  • Advanced analytics capabilities: BigQuery provides a range of advanced analytics capabilities, including machine learning integration, geospatial analysis, and support for SQL-based queries. It also offers a wide range of built-in functions and connectors for data exploration and visualization.

6 Cons of Google BigQuery

  • Steep learning curve: While BigQuery offers powerful capabilities, it can have a steep learning curve for users who are not familiar with cloud-based data analytics platforms. Users may need to invest time in understanding the query syntax and optimizing queries for performance.
  • Complex data modeling: BigQuery is a schema-less data warehouse, which means managing complex data models and relationships can be challenging. Designing efficient data models requires careful planning and understanding of the data structure.
  • Data movement costs: If you need to move data from external sources to BigQuery, there might be additional costs associated with data transfer. This can be a consideration if you have large volumes of data or frequent data updates.
  • Data size limitations: While BigQuery can handle massive amounts of data, there are certain limitations on individual table and query sizes. For example, a single query cannot process more than 100 TB of data, and a single table cannot exceed 20,000 columns.
  • Limited support for transactional operations: BigQuery is optimized for analytics workloads and doesn’t provide full support for transactional operations like traditional relational databases. It may not be suitable for use cases that require complex transaction processing or real-time data updates.
  • Dependency on internet connectivity: As a cloud-based service, BigQuery relies on a stable internet connection for access and data transfer. In case of network disruptions or limited connectivity, it can impact the availability and performance of your queries.

How and where is Google BigQuery used?

Case NameCase Description
1. Real-time AnalyticsGoogle BigQuery allows organizations to perform real-time analytics on large volumes of data. It enables businesses to analyze and derive insights from streaming data, such as website clicks, sensor data, and social media interactions, in near real-time. With BigQuery, companies can make data-driven decisions faster and respond to changing market conditions more effectively.
2. Data WarehousingBigQuery is an ideal solution for building a scalable and cost-effective data warehousing system. It can handle massive amounts of structured and semi-structured data, making it suitable for storing and analyzing historical data. By integrating BigQuery with other data processing tools, organizations can create a comprehensive data warehousing solution that meets their specific needs.
3. Machine LearningBigQuery provides a powerful platform for training and deploying machine learning models. It integrates seamlessly with popular machine learning frameworks, such as TensorFlow, allowing data scientists and developers to leverage the scalability and processing power of BigQuery to train models on large datasets. This enables organizations to unlock valuable insights and build predictive models to enhance decision-making processes.
4. Fraud DetectionBigQuery is capable of processing vast amounts of data in real-time, making it well-suited for fraud detection applications. By analyzing transactional data, user behavior patterns, and historical data, organizations can identify and mitigate fraudulent activities more efficiently. With the ability to process data at scale, BigQuery enables businesses to detect and prevent fraud in near real-time, minimizing financial losses.
5. IoT Data AnalyticsBigQuery can handle the high volume and velocity of data generated by IoT devices. It allows organizations to ingest, process, and analyze IoT data streams in real-time, enabling them to gain valuable insights and make data-driven decisions. By leveraging BigQuery’s capabilities, businesses can optimize operations, improve efficiency, and uncover new business opportunities in the rapidly expanding IoT ecosystem.
6. Marketing AnalyticsBigQuery enables marketers to analyze large datasets and derive actionable insights to optimize their marketing campaigns. By integrating data from various sources such as customer interactions, website analytics, and advertising platforms, marketers can gain a comprehensive view of their target audience and tailor their marketing strategies accordingly. BigQuery’s scalability and speed ensure that marketers can analyze vast amounts of data quickly and efficiently.
7. Log AnalysisBigQuery can be used for analyzing log data generated by applications, servers, and network devices. By centralizing log data in BigQuery, organizations can perform advanced analytics and gain visibility into system performance, identify anomalies, and troubleshoot issues more effectively. BigQuery’s fast querying capabilities and scalability make it an excellent choice for log analysis, allowing organizations to extract meaningful insights from log data.
8. Financial AnalysisBigQuery can handle complex financial data analysis tasks, such as risk assessment, portfolio management, and fraud detection in the financial sector. It allows financial institutions to analyze large volumes of financial data quickly, identify patterns, and make data-driven decisions to mitigate risks. BigQuery’s ability to process and query financial data at scale provides organizations with the necessary tools to gain insights and improve financial performance.

Let’s consider Difference between Junior, Middle, Senior, Expert/Team Lead developer roles.

Seniority NameYears of experienceResponsibilities and activitiesAverage salary (USD/year)
Junior Developer0-2 yearsAssisting senior developers in coding, debugging, and testing software applications. Learning and gaining experience in programming languages and development tools. Participating in code reviews and providing feedback. Working on smaller, well-defined tasks under the guidance of senior team members.40,000 – 60,000
Middle Developer2-5 yearsDeveloping software components and modules based on specifications. Collaborating with cross-functional teams to design and implement software solutions. Participating in code reviews and suggesting improvements. Mentoring junior developers and providing technical guidance. Working on medium-sized projects with moderate complexity.60,000 – 80,000
Senior Developer5-8 yearsLeading the development of complex software systems. Designing and architecting software solutions. Mentoring and coaching junior and middle developers. Collaborating with stakeholders to gather requirements and define project objectives. Participating in code reviews and ensuring adherence to coding standards. Solving technical challenges and providing innovative solutions.80,000 – 100,000
Expert/Team Lead8+ yearsLeading a team of developers and overseeing project execution. Providing technical leadership and guidance. Collaborating with product managers and stakeholders to define project scope and objectives. Conducting performance evaluations and identifying skill gaps. Making strategic decisions to enhance team productivity and efficiency. Working on large-scale projects with high complexity.100,000 – 150,000+

Hard skills of a Google BigQuery Developer

As a Google BigQuery Developer, you need to possess a range of hard skills to effectively work with this powerful data analytics platform.


  • Data Modeling: Ability to design and implement logical and physical data models in BigQuery.
  • SQL: Proficiency in writing SQL queries to retrieve, manipulate, and analyze data.
  • Data Warehousing: Understanding of data warehousing concepts and best practices in BigQuery.
  • ETL: Familiarity with Extract, Transform, Load (ETL) processes and tools for data integration.
  • Data Visualization: Knowledge of data visualization tools like Google Data Studio or Tableau for creating compelling visualizations.


  • Advanced SQL: Mastery of complex SQL queries, including subqueries, window functions, and advanced join techniques.
  • Performance Optimization: Ability to optimize query performance by analyzing query plans, using appropriate indexing, and partitioning data.
  • BigQuery ML: Experience with BigQuery ML for building and deploying machine learning models directly in BigQuery.
  • Data Pipeline: Proficiency in designing and building data pipelines using tools like Apache Beam or Google Cloud Dataflow.
  • Data Governance: Understanding of data governance principles and implementing security and access controls in BigQuery.
  • BigQuery APIs: Knowledge of BigQuery API integration for automating tasks and integrating BigQuery with other systems.
  • Data Quality Assurance: Ability to ensure data integrity and quality through data validation and reconciliation processes.


  • BigQuery Architecture: In-depth knowledge of BigQuery architecture and the ability to design scalable and efficient data solutions.
  • Data Partitioning: Expertise in partitioning data and using clustering techniques to optimize query performance.
  • Data Security: Experience in implementing advanced data security measures, including encryption, key management, and data masking.
  • Data Governance Framework: Establishing and maintaining a comprehensive data governance framework for BigQuery.
  • Advanced Analytics: Proficiency in advanced analytics techniques like predictive modeling, time series analysis, and anomaly detection.
  • Data Engineering: Extensive experience in building data engineering pipelines and workflows using tools like Apache Airflow or Google Cloud Composer.
  • Data Science Collaboration: Collaboration with data scientists to facilitate data exploration, feature engineering, and model deployment.
  • Cost Optimization: Ability to optimize BigQuery costs by implementing cost-saving strategies and monitoring usage patterns.

Expert/Team Lead

  • Data Strategy: Development and execution of a comprehensive data strategy aligned with business objectives.
  • Team Leadership: Experience in leading and managing a team of BigQuery developers, data engineers, and data scientists.
  • Data Governance Framework: Expertise in designing and implementing a robust data governance framework for the organization.
  • Performance Tuning: Advanced knowledge of performance tuning techniques to optimize query and data processing performance.
  • Advanced Security: Implementation of advanced security measures, including data classification, access controls, and auditing.
  • Cloud Architecture: Deep understanding of cloud architecture principles and the ability to design scalable and fault-tolerant solutions.
  • Data Lake Integration: Integration of BigQuery with data lakes and other data storage and processing systems.
  • BigQuery API Development: Development of custom solutions using BigQuery APIs for specific business needs.
  • BigQuery Data Transfer Service: Utilization of BigQuery Data Transfer Service for seamless data ingestion from various sources.
  • Advanced Data Analysis: Expertise in advanced data analysis techniques, including statistical modeling, data mining, and natural language processing.
  • Training and Mentoring: Providing training and mentorship to junior and middle-level BigQuery developers in the team.

What are top Google BigQuery instruments and tools?

  • BigQuery ML: BigQuery ML is a machine learning tool built into Google BigQuery that allows users to create and execute machine learning models directly within the BigQuery environment. It was introduced in 2018 and provides users with the ability to build and deploy machine learning models using SQL queries. This eliminates the need for data movement between different platforms and streamlines the machine learning workflow, making it more efficient and accessible.
  • Data Studio: Data Studio is a powerful data visualization and reporting tool that integrates seamlessly with Google BigQuery. It allows users to create interactive and customizable dashboards, reports, and data visualizations using a drag-and-drop interface. Data Studio supports real-time data updates and provides a wide range of visualization options, making it easy for users to gain insights from their BigQuery data and share them with others.
  • Cloud Datalab: Cloud Datalab is an interactive data exploration and analysis tool designed specifically for Google Cloud Platform, which includes integration with Google BigQuery. It provides a Jupyter notebook environment that allows users to write and execute Python code, query BigQuery data, and visualize results in a collaborative and interactive manner. Cloud Datalab supports multiple programming languages and provides pre-configured templates and examples, making it a versatile tool for data scientists and analysts.
  • Cloud Dataflow: Cloud Dataflow is a fully managed service for executing batch and streaming data processing pipelines. It offers a unified programming model and supports popular languages such as Java and Python. With its integration with Google BigQuery, users can easily ingest data from BigQuery into Dataflow pipelines for further processing and analysis. Cloud Dataflow’s auto-scaling capabilities and fault-tolerant processing make it an efficient tool for handling large-scale data processing tasks.
  • Cloud Composer: Cloud Composer is a fully managed workflow orchestration service that allows users to author, schedule, and monitor workflows across different services, including Google BigQuery. It provides a graphical interface for designing workflows and supports popular open-source tools such as Apache Airflow. With its integration with BigQuery, users can easily incorporate BigQuery queries and data transformations into their workflows, enabling them to automate complex data pipelines and data-driven processes.
  • Looker: Looker is a comprehensive data platform that offers data exploration, visualization, and collaboration capabilities. It integrates with Google BigQuery and provides a user-friendly interface for exploring and analyzing BigQuery data. Looker enables users to build and share interactive reports and dashboards, conduct ad-hoc analysis, and collaborate with team members. Its powerful data modeling capabilities allow users to create reusable data models and define business logic, making it a popular choice for organizations leveraging BigQuery for data analysis and reporting.
  • BigQuery BI Engine: BigQuery BI Engine is an in-memory analysis service that integrates with Google BigQuery. It allows users to perform interactive and high-performance analysis on large datasets stored in BigQuery, significantly reducing query latency. BI Engine provides sub-second query responses, making it ideal for real-time analytics and interactive dashboards. With its integration with popular BI tools such as Google Data Studio and Looker, users can seamlessly leverage BI Engine to accelerate their data exploration and visualization tasks.
  • BigQuery Data Transfer Service: BigQuery Data Transfer Service is a tool that simplifies the process of ingesting data from various sources into Google BigQuery. It provides pre-built connectors for popular data sources, such as Google Analytics, Google Ads, YouTube, and more. The Data Transfer Service automates data extraction, transformation, and loading (ETL) processes, allowing users to easily schedule and manage data transfers into BigQuery. This simplifies the data ingestion workflow and enables users to quickly analyze and derive insights from their data.

Soft skills of a Google BigQuery Developer

Soft skills are essential for a Google BigQuery Developer as they contribute to effective teamwork, communication, and problem-solving. These skills become increasingly important as one progresses from a Junior to an Expert/Team Lead level.


  • Attention to Detail: Precise execution of queries and analyzing data accurately.
  • Time Management: Meeting project deadlines and prioritizing tasks efficiently.
  • Adaptability: Quickly adjusting to new technologies and learning from feedback.
  • Collaboration: Working well with team members and seeking assistance when needed.
  • Communication: Clearly conveying ideas and updates to stakeholders.


  • Problem Solving: Identifying and resolving complex issues in BigQuery queries.
  • Data Analysis: Extracting meaningful insights from large datasets.
  • Leadership: Guiding junior team members and sharing best practices.
  • Critical Thinking: Evaluating different approaches and making informed decisions.
  • Project Management: Overseeing multiple projects and ensuring timely delivery.
  • Presentation Skills: Communicating findings and recommendations effectively.
  • Client Management: Building strong relationships and understanding client needs.


  • Strategic Thinking: Developing long-term plans and aligning them with business goals.
  • Mentorship: Coaching and mentoring junior and middle-level developers.
  • Innovation: Identifying opportunities to optimize BigQuery performance and efficiency.
  • Team Building: Fostering a collaborative and inclusive work environment.
  • Stakeholder Management: Engaging with stakeholders at all levels of the organization.
  • Conflict Resolution: Resolving conflicts and promoting a positive team dynamic.
  • Quality Assurance: Ensuring data accuracy and maintaining high standards.
  • Continuous Learning: Keeping up-to-date with advancements in BigQuery and data analytics.

Expert/Team Lead

  • Strategic Planning: Setting the technical direction and roadmap for the team.
  • Decision-Making: Making critical decisions that impact the overall project success.
  • Resource Allocation: Optimizing resources and assigning tasks effectively.
  • Risk Management: Identifying and mitigating risks in complex projects.
  • Thought Leadership: Contributing to the development of industry best practices.
  • Business Acumen: Understanding the business context and aligning solutions accordingly.
  • Negotiation Skills: Negotiating contracts and agreements with clients and vendors.
  • Performance Management: Evaluating team performance and providing constructive feedback.
  • Continuous Improvement: Driving process improvements and enhancing productivity.
  • Technical Expertise: Demonstrating deep knowledge of BigQuery and related technologies.
  • Team Collaboration: Facilitating effective collaboration between cross-functional teams.

TOP 12 Tech facts and history of creation and versions about Google BigQuery Development

  • Google BigQuery was created in 2010 as a fully-managed, serverless data warehouse solution.
  • It was developed by Google engineers Femi Olumofin and Chad W. Jennings.
  • BigQuery leverages Google’s Dremel technology, which allows for fast, interactive analysis of large datasets.
  • One of BigQuery’s groundbreaking features is its ability to process massive amounts of data in seconds or minutes, thanks to its distributed architecture.
  • In 2011, BigQuery was made available to the public as a service.
  • BigQuery supports SQL-like queries, making it accessible to users familiar with traditional database systems.
  • It offers a scalable and flexible storage system, allowing users to easily load and analyze petabytes of data.
  • Google BigQuery is integrated with other Google Cloud Platform services, enabling seamless data analysis across various tools and services.
  • BigQuery supports real-time streaming ingestion of data, allowing for immediate analysis of constantly changing datasets.
  • BigQuery’s security model includes fine-grained access controls, encryption at rest and in transit, and audit logs for compliance.
  • Over the years, Google continuously improved BigQuery’s performance, introducing features like automatic query optimization and caching.
  • BigQuery has multiple versions, including a free tier (limited usage) and a paid tier with various pricing options based on usage and storage.

TOP 12 Facts about Google BigQuery

  • Google BigQuery is a fully managed, serverless data warehouse and analytics platform that enables users to analyze massive datasets in real-time using SQL queries.
  • It is capable of handling petabytes of data, making it one of the most scalable data warehousing solutions available.
  • BigQuery uses a columnar storage format, which allows for faster query performance by only reading the columns needed for a particular query.
  • It supports a wide range of data formats, including CSV, JSON, Avro, Parquet, and more, making it easy to ingest and analyze data from various sources.
  • BigQuery is designed to be highly available and reliable, with built-in replication and automated backups to ensure data durability.
  • It offers built-in integration with other Google Cloud services, such as Google Cloud Storage, Google Cloud Dataproc, and Google Cloud Dataflow, allowing users to easily ingest, process, and analyze data in a unified environment.
  • BigQuery provides a flexible pricing model based on on-demand usage, allowing users to pay only for the resources they consume without any upfront costs or long-term commitments.
  • It offers an extensive set of SQL functions and advanced analytical capabilities, including window functions, approximate aggregation, and machine learning integration, enabling users to perform complex data analysis tasks.
  • BigQuery provides a powerful web UI, command-line tools, and APIs, making it accessible to both data analysts and developers for querying, managing, and automating data workflows.
  • It supports data encryption at rest and in transit, ensuring the security and privacy of sensitive information stored in BigQuery.
  • BigQuery has a strong ecosystem with various third-party tools and integrations, allowing users to leverage their existing data stack and extend BigQuery’s capabilities.
  • Google BigQuery is widely adopted by organizations of all sizes and industries, including Fortune 500 companies, startups, and academic institutions, to gain actionable insights from their data.

Cases when Google BigQuery does not work

  1. Insufficient Data: Google BigQuery is designed to handle large volumes of data efficiently. However, if you have a very small dataset with just a few rows or a low volume of data, BigQuery may not be the most cost-effective or efficient solution for your needs. In such cases, using a traditional database or other data processing tools might be more appropriate.
  2. Complex Transactional Workloads: BigQuery is primarily built for analytical workloads rather than handling complex transactional operations. If your use case involves frequent updates, inserts, or deletes on individual rows, you might find that a traditional relational database management system (RDBMS) like MySQL or PostgreSQL is better suited for your requirements.
  3. Real-Time Data Processing: Although BigQuery offers high-speed querying capabilities, it is not designed for real-time data processing. If your use case demands immediate or near-real-time analysis of streaming data, you might want to explore other technologies like Apache Kafka, Apache Flink, or Google Cloud Dataflow.
  4. High Latency Tolerance: While BigQuery provides impressive scalability and parallelism for processing large datasets, it is not optimized for low-latency queries. If your application requires sub-second response times, consider using an in-memory database or a caching layer to improve query performance.
  5. Strict Data Consistency Requirements: BigQuery is a distributed system that uses eventual consistency, which means it does not guarantee strong data consistency at all times. If your use case relies heavily on strict data consistency, consider using a traditional RDBMS that provides ACID (Atomicity, Consistency, Isolation, Durability) guarantees.
  6. Limited Control Over Infrastructure: BigQuery is a fully managed service offered by Google Cloud, which means you have limited control over the underlying infrastructure. If your use case requires fine-grained control over hardware configurations, operating systems, or network settings, you might prefer managing your own infrastructure using tools like Apache Hadoop or Apache Spark.
  7. High Cost for Small Workloads: While BigQuery is cost-effective for large-scale data processing, it may not be the most economical option for small workloads or sporadic queries. If you have a low volume of data or infrequent analytical needs, consider using on-demand pricing or exploring alternative solutions like Google Cloud Dataprep or Google Sheets.
  8. Data Privacy and Compliance: If your data has strict privacy or compliance requirements, such as HIPAA or GDPR, you need to ensure that BigQuery meets all the necessary security and compliance standards. While Google Cloud provides robust security measures, you should carefully evaluate the specific data protection requirements for your use case.

TOP 10 Google BigQuery Related Technologies

  • SQL

    SQL (Structured Query Language) is the most fundamental programming language used in Google BigQuery. It allows developers to interact with databases, retrieve and manipulate data efficiently.

  • Python

    Python is a versatile and widely used programming language for data analysis and manipulation. It offers a variety of libraries and tools that integrate well with BigQuery, making it a popular choice for software development.

  • Java

    Java is a robust and widely adopted programming language known for its scalability and performance. It has extensive support for BigQuery through various client libraries, making it a preferred language for enterprise-level applications.

  • R

    R is a powerful language for statistical computing and data analysis. It has dedicated packages and libraries that enable seamless integration with BigQuery, allowing users to perform advanced analytics and visualizations.

  • JavaScript

    JavaScript is a versatile scripting language commonly used in web development. It offers client-side and server-side frameworks such as Node.js, which can interact with BigQuery through APIs, making it suitable for building real-time data applications.

  • Apache Spark

    Apache Spark is a fast and distributed data processing framework that can seamlessly integrate with BigQuery. It provides a unified analytics engine and supports various programming languages, making it ideal for large-scale data processing and machine learning tasks.

  • TensorFlow

    TensorFlow is an open-source machine learning framework developed by Google. It integrates with BigQuery to enable deep learning and advanced analytics on large datasets. Its flexibility and scalability make it a popular choice for building AI-driven applications.

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