Zion Tech Group

Tag: pipelines

  • Data Observability for Data Engineering: Proactive strategies for ensuring data accuracy and addressing broken data pipelines

    Data Observability for Data Engineering: Proactive strategies for ensuring data accuracy and addressing broken data pipelines


    Price: $36.99
    (as of Nov 28,2024 09:16:39 UTC – Details)




    Publisher ‏ : ‎ Packt Publishing (December 29, 2023)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 228 pages
    ISBN-10 ‏ : ‎ 1804616028
    ISBN-13 ‏ : ‎ 978-1804616024
    Item Weight ‏ : ‎ 14.4 ounces
    Dimensions ‏ : ‎ 9.25 x 7.52 x 0.48 inches


    Data observability is a crucial aspect of data engineering that involves monitoring, managing, and ensuring the accuracy and reliability of data pipelines. In today’s data-driven world, it is essential to have proactive strategies in place to detect and address any issues that may arise in data pipelines.

    One of the key challenges in data engineering is ensuring data accuracy and reliability. Broken data pipelines can lead to incorrect insights, wasted resources, and ultimately, a loss of trust in the data. To prevent these issues, data engineers need to implement proactive strategies for data observability.

    One proactive strategy is to establish a robust monitoring system that tracks the performance and health of data pipelines in real-time. This monitoring system should include alerts and notifications that notify data engineers of any anomalies or failures in the data pipeline. By proactively monitoring data pipelines, data engineers can quickly identify and address issues before they impact the accuracy of the data.

    Another proactive strategy is to implement data quality checks at various stages of the data pipeline. These checks can include validation of data formats, data completeness, and data consistency. By implementing data quality checks, data engineers can ensure that the data flowing through the pipeline is accurate and reliable.

    Furthermore, data engineers should also prioritize data lineage and metadata management to track the flow of data through the pipeline and understand the origin of the data. By maintaining a clear understanding of data lineage, data engineers can quickly trace back any issues to their source and address them effectively.

    In conclusion, data observability is essential for data engineering to ensure data accuracy and reliability. By implementing proactive strategies such as real-time monitoring, data quality checks, and data lineage management, data engineers can prevent broken data pipelines and maintain the trustworthiness of the data.
    #Data #Observability #Data #Engineering #Proactive #strategies #ensuring #data #accuracy #addressing #broken #data #pipelines

  • Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning

    Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning


    Price: $79.99 – $57.99
    (as of Nov 27,2024 08:12:36 UTC – Details)


    From the brand

    oreillyoreilly

    Explore more Data Science

    OreillyOreilly

    Sharing the knowledge of experts

    O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.

    Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

    Publisher ‏ : ‎ O’Reilly Media; 2nd edition (May 3, 2022)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 459 pages
    ISBN-10 ‏ : ‎ 1098118952
    ISBN-13 ‏ : ‎ 978-1098118952
    Item Weight ‏ : ‎ 2.31 pounds
    Dimensions ‏ : ‎ 7 x 1 x 9.25 inches


    Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines

    Data science is a rapidly growing field that involves extracting insights and knowledge from data to drive decision-making and innovation. With the rise of big data and the need for real-time analytics, organizations are looking for ways to build scalable and efficient data pipelines to process and analyze their data.

    One of the key players in the data science space is the Google Cloud Platform (GCP), which offers a suite of tools and services for building, deploying, and managing data pipelines. In this post, we will explore how to implement end-to-end real-time data pipelines on GCP, from data ingestion to machine learning.

    Data Ingestion:
    The first step in building a data pipeline is to ingest data from various sources, such as databases, APIs, and streaming platforms. GCP provides a number of tools for data ingestion, including Cloud Pub/Sub for real-time messaging, Cloud Storage for batch processing, and Cloud Dataflow for stream processing.

    Data Processing:
    Once the data is ingested, it needs to be processed and transformed before it can be analyzed. GCP offers tools like Cloud Dataprep for data preparation and cleansing, Cloud Dataflow for batch and stream processing, and BigQuery for data warehousing and analytics.

    Machine Learning:
    After the data has been processed, it can be used to train machine learning models for predictive analytics and decision-making. GCP provides a range of machine learning tools, including BigQuery ML for SQL-based machine learning, TensorFlow for deep learning, and AutoML for automated model training.

    Monitoring and Optimization:
    Finally, it is important to monitor and optimize the performance of the data pipeline to ensure that it meets the required SLAs and delivers insights in a timely manner. GCP offers tools like Cloud Monitoring for real-time monitoring and logging, Cloud Trace for performance profiling, and Cloud Profiler for code optimization.

    In conclusion, building real-time data pipelines on the Google Cloud Platform involves a combination of data ingestion, processing, machine learning, and monitoring. By leveraging the tools and services provided by GCP, organizations can build scalable and efficient data pipelines to drive data-driven decision-making and innovation.
    #Data #Science #Google #Cloud #Platform #Implementing #EndtoEnd #RealTime #Data #Pipelines #Ingest #Machine #Learning

  • Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines

    Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines


    Price: $79.99 – $64.64
    (as of Nov 27,2024 07:20:36 UTC – Details)


    From the brand

    oreillyoreilly

    Explore our collection

    OreillyOreilly

    Sharing the knowledge of experts

    O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.

    Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

    Publisher ‏ : ‎ O’Reilly Media; 1st edition (November 5, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 472 pages
    ISBN-10 ‏ : ‎ 1098156013
    ISBN-13 ‏ : ‎ 978-1098156015
    Item Weight ‏ : ‎ 1.65 pounds
    Dimensions ‏ : ‎ 7 x 0.95 x 9.19 inches


    Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines

    In today’s data-driven world, machine learning has become a crucial tool for businesses looking to gain insights and make better decisions. However, building and deploying machine learning models in production can be a complex and challenging process.

    Machine learning production systems are responsible for the end-to-end process of developing, deploying, and maintaining machine learning models in a production environment. This involves not only building and training models, but also managing data pipelines, monitoring model performance, and ensuring that models are deployed and scaled effectively.

    One key component of machine learning production systems is the engineering of machine learning models and pipelines. This involves designing and implementing the architecture and infrastructure needed to train, test, and deploy machine learning models at scale.

    Engineers working on machine learning production systems need to have a deep understanding of machine learning algorithms, as well as experience in software engineering and data infrastructure. They must be able to design and implement robust, scalable machine learning pipelines that can handle large volumes of data and be deployed in a production environment.

    In addition, engineers working on machine learning production systems need to have a strong understanding of best practices for model evaluation, monitoring, and maintenance. They must be able to ensure that models are performing as expected, and be able to quickly identify and address any issues that arise.

    Overall, building and deploying machine learning models in production requires a combination of technical skills, domain knowledge, and a deep understanding of machine learning algorithms and best practices. By engineering robust machine learning models and pipelines, businesses can harness the power of machine learning to drive better decision-making and gain a competitive edge in today’s data-driven world.
    #Machine #Learning #Production #Systems #Engineering #Machine #Learning #Models #Pipelines

  • Cost-Effective Data Pipelines: Balancing Trade-Offs When Developing Pipelines in the Cloud

    Cost-Effective Data Pipelines: Balancing Trade-Offs When Developing Pipelines in the Cloud


    Price: $65.99 – $40.99
    (as of Nov 25,2024 04:06:08 UTC – Details)


    From the brand

    oreillyoreilly

    Databases, data science & more

    OreillyOreilly

    Sharing the knowledge of experts

    O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.

    Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

    Publisher ‏ : ‎ O’Reilly Media; 1st edition (August 22, 2023)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 286 pages
    ISBN-10 ‏ : ‎ 1492098647
    ISBN-13 ‏ : ‎ 978-1492098645
    Item Weight ‏ : ‎ 1.02 pounds
    Dimensions ‏ : ‎ 7 x 0.6 x 9.19 inches


    Cost-Effective Data Pipelines: Balancing Trade-Offs When Developing Pipelines in the Cloud

    Developing data pipelines in the cloud offers numerous benefits, including scalability, flexibility, and ease of integration with other cloud services. However, it is important to strike a balance between cost and performance when designing and implementing these pipelines.

    One of the key trade-offs to consider is the choice between managed and self-managed services. Managed services, such as AWS Glue or Google Cloud Dataflow, offer convenience and ease of use, but they can be more costly than self-managed solutions. On the other hand, self-managed solutions, such as Apache Airflow or Kubernetes, require more technical expertise and maintenance but can be more cost-effective in the long run.

    Another trade-off to consider is the choice between batch and streaming data processing. Batch processing is typically more cost-effective and simpler to implement, but it may not be suitable for real-time or near-real-time applications. Streaming data processing, on the other hand, offers real-time insights but can be more complex and costly to set up and maintain.

    Furthermore, optimizing data storage and processing resources can help reduce costs. For example, using cheaper storage options like Amazon S3 or Google Cloud Storage instead of expensive databases can lead to significant cost savings. Additionally, using serverless computing services like AWS Lambda or Google Cloud Functions can help eliminate the need to provision and manage servers, reducing operational costs.

    In conclusion, when developing data pipelines in the cloud, it is crucial to carefully consider the trade-offs between cost and performance. By choosing the right mix of managed and self-managed services, batch and streaming processing, and optimizing storage and processing resources, organizations can create cost-effective data pipelines that meet their business needs.
    #CostEffective #Data #Pipelines #Balancing #TradeOffs #Developing #Pipelines #Cloud

  • Data Engineering with AWS – Second Edition: Acquire the skills to design and build AWS-based data transformation pipelines like a pro

    Data Engineering with AWS – Second Edition: Acquire the skills to design and build AWS-based data transformation pipelines like a pro


    Price: $51.99 – $41.59
    (as of Nov 24,2024 07:26:10 UTC – Details)


    From the brand

    Packt Brand ImagePackt Brand Image

    Packt LogoPackt Logo

    Packt is a leading publisher of technical learning content with the ability to publish books on emerging tech faster than any other.

    Our mission is to increase the shared value of deep tech knowledge by helping tech pros put software to work.

    We help the most interesting minds and ground-breaking creators on the planet distill and share the working knowledge of their peers.

    See Our Full Range

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    Publisher ‏ : ‎ Packt Publishing; 2nd ed. edition (October 31, 2023)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 636 pages
    ISBN-10 ‏ : ‎ 1804614424
    ISBN-13 ‏ : ‎ 978-1804614426
    Item Weight ‏ : ‎ 2.4 pounds
    Dimensions ‏ : ‎ 9.25 x 7.52 x 1.31 inches


    Are you looking to enhance your data engineering skills with Amazon Web Services (AWS)? Look no further than the second edition of “Data Engineering with AWS”! In this updated version, you will acquire the skills needed to design and build AWS-based data transformation pipelines like a pro.

    Whether you are a beginner or an experienced data engineer, this book will provide you with practical guidance on using AWS services such as S3, Glue, Athena, Redshift, and more to build efficient and scalable data pipelines. You will learn how to optimize data ingestion, transformation, and storage processes, as well as how to monitor and troubleshoot your pipelines for optimal performance.

    With real-world examples and hands-on exercises, “Data Engineering with AWS” will help you master the tools and techniques needed to excel in the world of data engineering. So, why wait? Start your journey to becoming a data engineering pro with AWS today!
    #Data #Engineering #AWS #Edition #Acquire #skills #design #build #AWSbased #data #transformation #pipelines #pro

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