Google Cloud Training Institute in Hyderabad
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Visualpath is the Leading and Best GCP Data Engineer Online Training Training institute in Hyderabad. We are Providing Live Instructor Led Online Training Classes delivered by experts from the industry using Real-time projects. Call on - +91-9989971070. WhatsApp : https://www.whatsapp.com/catalog/919989971070/ Visit: https://www.visualpath.in/GCP-Data-Engineer-online-traning.html
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GCP Online Training | GCP Data Engineer Online Course
Google Cloud Training Institute in Hyderabad, India. Visualpath provides Best GCP Data Engineer Online Training by IT 5-10+ yr in industrial real time experts.
Telegram: https://t.me/+bEu9LVFFlh5iOTA9
WhatsApp : https://www.whatsapp.com/catalog/919989971070/
Visit : https://www.visualpath.in/gcp-data-engineering-online-traning.html
Data Platform on GCP
The document provides an overview of Google Cloud's data platform and big data portfolio. It discusses Google Cloud Platform and its various data storage and database services like Cloud Storage, Cloud Bigtable, Cloud Datastore, Cloud SQL, Cloud Spanner, and BigQuery. It then summarizes each service's ideal use cases. The document also presents Google Cloud's big data reference architectures and data science reference architecture. It concludes by highlighting BigQuery's advantages over other data warehouse solutions and providing a link to a BigQuery hands-on lab.
Workshop on Google Cloud Data Platform
The document provides an agenda and information about a GoDataFest workshop on Google Cloud Platform for data. The agenda includes an introduction to GCP for data, a session on roles and tools on GCP for different data roles, and a session where participants will build projects on GCP in mixed workgroups. It outlines the goals and tools used by different roles like data engineer, analytics engineer, and Looker user. It also provides information on Google Cloud technologies like BigQuery, Dataform, Looker, and how they fit into the modern data lifecycle and platform. Participants are then divided into mixed workgroups based on their preferred role and given insights to explore in their projects.
IBM Cloud Day January 2021 - A well architected data lake
- The document discusses an IBM Cloud Day 2021 event focused on well-architected data lakes. It provides an overview of two sessions on data lake architecture and building a cloud native data lake on IBM Cloud.
- It also summarizes the key capabilities organizations need from a data lake, including visualizing data, flexibility/accessibility, governance, and gaining insights. Cloud data lakes can address these needs for various roles.
Big query
Google BigQuery is Google's fully managed big data analytics service that allows users to analyze very large datasets. It offers a fast and easy to use service with no infrastructure to manage. Developers can stream up to 100,000 rows of data per second for near real-time analysis. BigQuery bills users per project on a pay-as-you-go model, with the first 1TB of data processed each month free of charge.
GOOGLE CLOUD DATA AND STORAGE Foundations.docx
Google Cloud Platform (GCP) is a suite of cloud computing services offered by Google. It provides a wide range of infrastructure and platform services for building, deploying, and scaling applications and services.
With Automated ML, is Everyone an ML Engineer?
Google AutoML, AWS SageMaker and other ML tools automate some but not all steps in machine learning workflows. Learn about problem formulation, data engineering, monitoring, and fairness assessment.
Data Platform Architecture Principles and Evaluation Criteria
There are various platforms, SaaS offerings, tools, opensource available in market, while choosing the best one is always a challenge.
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Google Cloud Training Institute in Hyderabad, India. Visualpath provides Best GCP Data Engineer Online Training by IT 5-10+ yr in industrial real time experts.
Telegram: https://t.me/+bEu9LVFFlh5iOTA9
WhatsApp : https://www.whatsapp.com/catalog/919989971070/
Visit : https://www.visualpath.in/gcp-data-engineering-online-traning.html
Data Platform on GCP
The document provides an overview of Google Cloud's data platform and big data portfolio. It discusses Google Cloud Platform and its various data storage and database services like Cloud Storage, Cloud Bigtable, Cloud Datastore, Cloud SQL, Cloud Spanner, and BigQuery. It then summarizes each service's ideal use cases. The document also presents Google Cloud's big data reference architectures and data science reference architecture. It concludes by highlighting BigQuery's advantages over other data warehouse solutions and providing a link to a BigQuery hands-on lab.
Workshop on Google Cloud Data Platform
The document provides an agenda and information about a GoDataFest workshop on Google Cloud Platform for data. The agenda includes an introduction to GCP for data, a session on roles and tools on GCP for different data roles, and a session where participants will build projects on GCP in mixed workgroups. It outlines the goals and tools used by different roles like data engineer, analytics engineer, and Looker user. It also provides information on Google Cloud technologies like BigQuery, Dataform, Looker, and how they fit into the modern data lifecycle and platform. Participants are then divided into mixed workgroups based on their preferred role and given insights to explore in their projects.
IBM Cloud Day January 2021 - A well architected data lake
- The document discusses an IBM Cloud Day 2021 event focused on well-architected data lakes. It provides an overview of two sessions on data lake architecture and building a cloud native data lake on IBM Cloud.
- It also summarizes the key capabilities organizations need from a data lake, including visualizing data, flexibility/accessibility, governance, and gaining insights. Cloud data lakes can address these needs for various roles.
Big query
Google BigQuery is Google's fully managed big data analytics service that allows users to analyze very large datasets. It offers a fast and easy to use service with no infrastructure to manage. Developers can stream up to 100,000 rows of data per second for near real-time analysis. BigQuery bills users per project on a pay-as-you-go model, with the first 1TB of data processed each month free of charge.
GOOGLE CLOUD DATA AND STORAGE Foundations.docx
Google Cloud Platform (GCP) is a suite of cloud computing services offered by Google. It provides a wide range of infrastructure and platform services for building, deploying, and scaling applications and services.
With Automated ML, is Everyone an ML Engineer?
Google AutoML, AWS SageMaker and other ML tools automate some but not all steps in machine learning workflows. Learn about problem formulation, data engineering, monitoring, and fairness assessment.
Data Platform Architecture Principles and Evaluation Criteria
There are various platforms, SaaS offerings, tools, opensource available in market, while choosing the best one is always a challenge.
[Study Guide] Google Professional Cloud Architect (GCP-PCA) Certification
Start Here---> https://bit.ly/3bGEd9l <---Get complete detail on GCP-PCA exam guide to crack Professional Cloud Architect. You can collect all information on GCP-PCA tutorial, practice test, books, study material, exam questions, and syllabus. Firm your knowledge on Professional Cloud Architect and get ready to crack GCP-PCA certification. Explore all information on GCP-PCA exam with the number of questions, passing percentage, and time duration to complete the test.
Introduction to Data Engineer and Data Pipeline at Credit OK
The document discusses the role of data engineers and data pipelines. It begins with an introduction to big data and why data volumes are increasing. It then covers what data engineers do, including building data architectures, working with cloud infrastructure, and programming for data ingestion, transformation, and loading. The document also explains data pipelines, describing extract, transform, load (ETL) processes and batch versus streaming data. It provides an example of Credit OK's data pipeline architecture on Google Cloud Platform that extracts raw data from various sources, cleanses and loads it into BigQuery, then distributes processed data to various applications. It emphasizes the importance of data engineers in processing and managing large, complex data sets.
ADV Slides: Building and Growing Organizational Analytics with Data Lakes
Data lakes are providing immense value to organizations embracing data science.
In this webinar, William will discuss the value of having broad, detailed, and seemingly obscure data available in cloud storage for purposes of expanding Data Science in the organization.
Big data in action
The document discusses Google Cloud Platform and its capabilities for big data and analytics. It notes that Google Cloud Platform is built on Google's infrastructure which powers its own services and has 17 years of experience building cloud infrastructure. It then summarizes several key services including Compute Engine, App Engine, BigQuery, Cloud Dataflow, and Cloud Dataproc that can be used for infrastructure, platforms, software, as well as big data, analytics, and machine learning.
Building a Turbo-fast Data Warehousing Platform with Databricks
Traditionally, data warehouse platforms have been perceived as cost prohibitive, challenging to maintain and complex to scale. The combination of Apache Spark and Spark SQL – running on AWS – provides a fast, simple, and scalable way to build a new generation of data warehouses that revolutionizes how data scientists and engineers analyze their data sets.
In this webinar you will learn how Databricks - a fully managed Spark platform hosted on AWS - integrates with variety of different AWS services, Amazon S3, Kinesis, and VPC. We’ll also show you how to build your own data warehousing platform in very short amount of time and how to integrate it with other tools such as Spark’s machine learning library and Spark streaming for real-time processing of your data.
Knowledge Graph for Machine Learning and Data Science
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
Demystifying Data Warehouse as a Service (DWaaS)
This is from the talk I gave at the 30th Anniversary NoCOUG meeting in San Jose, CA.
We all know that data warehouses and best practices for them are changing dramatically today. As organizations build new data warehouses and modernize established ones, they are turning to Data Warehousing as a Service (DWaaS) in hopes of taking advantage of the performance, concurrency, simplicity, and lower cost of a SaaS solution or simply to reduce their data center footprint (and the maintenance that goes with that).
But what is a DWaaS really? How is it different from traditional on-premises data warehousing?
In this talk I will:
• Demystify DWaaS by defining it and its goals
• Discuss the real-world benefits of DWaaS
• Discuss some of the coolest features in a DWaaS solution as exemplified by the Snowflake Elastic Data Warehouse.
Cloud Developer Days - BigQuery
This document provides an overview and agenda for a presentation on Google BigQuery. It includes sections on BigQuery internals and architecture, use cases, and demos. The presentation will cover an overview of Google Cloud Platform and BigQuery, their internals including storage, execution engines, and compute resources, common use cases, and end with a demo in Google Cloud Platform console and BigQuery. It is presented by an expert in BI, analytics, and cloud platforms.
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Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
Google Cloud Dataflow
Google Cloud Dataflow is a unified streaming and batch data processing service that allows users to set up pipelines to process data in various patterns. It features both horizontal and vertical autoscaling, real-time monitoring, and integration with other frameworks. Getting started involves choosing a programming model like Java, Python, or SQL and running sample pipelines locally or on the Cloud Dataflow service. Use cases include stream analytics, real-time AI, log and sensor data processing. The documentation provides more in-depth information on features and usage.
Resume_of_Vasudevan - Hadoop
Vasudevan Venkatraman has over 11 years of experience working in the IT industry, including 7+ years of experience with Oracle PL/SQL, data warehousing, and 3+ years in performance consulting and applications database administration. He has experience designing and developing applications using Oracle PL/SQL, Hadoop, and big data technologies. Currently he works as an Assistant Consultant at TCS focusing on data warehousing projects using Oracle and Hadoop.
Executive Intro to BigQuery
A high-level overview of Google BigQuery. What it is, what's behind it, and a small R script example of what it can do. Prepared by William M. Cohee
Building End-to-End Delta Pipelines on GCP
Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years.
Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines.
In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.
CDS Overview (May 2015)
The document discusses IBM's cloud data services and analytics offerings. It introduces IBM Cloudant for NoSQL database services, IBM dashDB for a cloud data warehouse with built-in analytics, and how they can be used together. Use cases are provided showing how a payment processor leveraged Cloudant's geospatial capabilities, an investment firm used Cloudant and dashDB to enable real-time access to analytics, and a food distributor analyzed sales data from different business units stored in dashDB.
Integrating Google Cloud Dataproc with Alluxio for faster performance in the ...
Google Dataproc is Google Cloud's fully managed Apache Spark and Apache Hadoop service. Alluxio is an open source data orchestration platform that can be used with Dataproc to accelerate analytics workloads. With a single initialization action, Alluxio can be installed on a Dataproc cluster to cache data from Cloud Storage for faster queries. Alluxio also enables "zero-copy bursting" of workloads to the cloud by allowing frameworks to access data directly from remote HDFS without needing to copy it. This provides elastic compute capacity while avoiding high network latency and bandwidth costs of copying large datasets.
Druid Optimizations for Scaling Customer Facing Analytics
Druid was implemented at Conviva to improve their streaming analytics capabilities. Previously they used Hadoop batch jobs and Spark streaming, but saw challenges with reliability and performance like query timeouts. With Druid they saw improvements through optimizations like data locality tuning, additional query tiers, and Kubernetes improvements on Google Cloud. This helped add a "9" to their reliability. However, challenges remained around cost, high cardinality queries, and rapid disaster recovery which they continue working to address.
Google Cloud Platform at Vente-Exclusive.com
This document describes Vente-Exclusive's use of Google Cloud Platform technologies like Google Cloud Storage, Cloud DataProc, BigQuery, Cloud Pub/Sub, Cloud DataFlow and App Engine for their big data analytics processes. It discusses how they leverage these services for ETL, batch and stream processing of user behavior and transactional data to power analytics dashboards and optimize various business metrics like reducing returns, improving customer insights and forecasting.
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Visualpath provides top-quality Certified Kubernetes Security Specialist Training Worldwide led by real-time instructors. We offer daily recordings and presentations for reference. Enroll for a Free Demo. Call +91-9989971070.
Visit Blog: https://visualpathblogs.com/
WhatsApp: https://www.whatsapp.com/catalog/917032290546/
Visit: https://www.visualpath.in/DevOps-docker-kubernetes-training.html
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Traditionally, data warehouse platforms have been perceived as cost prohibitive, challenging to maintain and complex to scale. The combination of Apache Spark and Spark SQL – running on AWS – provides a fast, simple, and scalable way to build a new generation of data warehouses that revolutionizes how data scientists and engineers analyze their data sets.
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At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
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This is from the talk I gave at the 30th Anniversary NoCOUG meeting in San Jose, CA.
We all know that data warehouses and best practices for them are changing dramatically today. As organizations build new data warehouses and modernize established ones, they are turning to Data Warehousing as a Service (DWaaS) in hopes of taking advantage of the performance, concurrency, simplicity, and lower cost of a SaaS solution or simply to reduce their data center footprint (and the maintenance that goes with that).
But what is a DWaaS really? How is it different from traditional on-premises data warehousing?
In this talk I will:
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• Discuss the real-world benefits of DWaaS
• Discuss some of the coolest features in a DWaaS solution as exemplified by the Snowflake Elastic Data Warehouse.
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This document provides an overview and agenda for a presentation on Google BigQuery. It includes sections on BigQuery internals and architecture, use cases, and demos. The presentation will cover an overview of Google Cloud Platform and BigQuery, their internals including storage, execution engines, and compute resources, common use cases, and end with a demo in Google Cloud Platform console and BigQuery. It is presented by an expert in BI, analytics, and cloud platforms.
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Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
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Google Cloud Dataflow is a unified streaming and batch data processing service that allows users to set up pipelines to process data in various patterns. It features both horizontal and vertical autoscaling, real-time monitoring, and integration with other frameworks. Getting started involves choosing a programming model like Java, Python, or SQL and running sample pipelines locally or on the Cloud Dataflow service. Use cases include stream analytics, real-time AI, log and sensor data processing. The documentation provides more in-depth information on features and usage.
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Executive Intro to BigQuery
A high-level overview of Google BigQuery. What it is, what's behind it, and a small R script example of what it can do. Prepared by William M. Cohee
Building End-to-End Delta Pipelines on GCP
Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years.
Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines.
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The document discusses IBM's cloud data services and analytics offerings. It introduces IBM Cloudant for NoSQL database services, IBM dashDB for a cloud data warehouse with built-in analytics, and how they can be used together. Use cases are provided showing how a payment processor leveraged Cloudant's geospatial capabilities, an investment firm used Cloudant and dashDB to enable real-time access to analytics, and a food distributor analyzed sales data from different business units stored in dashDB.
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Google Dataproc is Google Cloud's fully managed Apache Spark and Apache Hadoop service. Alluxio is an open source data orchestration platform that can be used with Dataproc to accelerate analytics workloads. With a single initialization action, Alluxio can be installed on a Dataproc cluster to cache data from Cloud Storage for faster queries. Alluxio also enables "zero-copy bursting" of workloads to the cloud by allowing frameworks to access data directly from remote HDFS without needing to copy it. This provides elastic compute capacity while avoiding high network latency and bandwidth costs of copying large datasets.
Druid Optimizations for Scaling Customer Facing Analytics
Druid was implemented at Conviva to improve their streaming analytics capabilities. Previously they used Hadoop batch jobs and Spark streaming, but saw challenges with reliability and performance like query timeouts. With Druid they saw improvements through optimizations like data locality tuning, additional query tiers, and Kubernetes improvements on Google Cloud. This helped add a "9" to their reliability. However, challenges remained around cost, high cardinality queries, and rapid disaster recovery which they continue working to address.
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This document describes Vente-Exclusive's use of Google Cloud Platform technologies like Google Cloud Storage, Cloud DataProc, BigQuery, Cloud Pub/Sub, Cloud DataFlow and App Engine for their big data analytics processes. It discusses how they leverage these services for ETL, batch and stream processing of user behavior and transactional data to power analytics dashboards and optimize various business metrics like reducing returns, improving customer insights and forecasting.
Columbia Migrates from Legacy Data Warehouse to an Open Data Platform with De...
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Visualpath is the best Kubernetes Training Institute in Hyderabad. We are providing Online & Classroom Training classes by real-time faculty with real time Projects. You will get the best course at an affordable cost. Call on - +91-9989971070.
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Visualpath is the best DevOps Online Training Institute in Hyderabad. We are providing Online & Classroom Training classes by real-time faculty with real time Projects. DevOps Training Online. You will get the best course at an affordable cost. Call on - +91-9989971070.
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Visualpath is the Leading and Best Docker Online Training Institute in Hyderabad. We are Providing Live Instructor-Led Online & Classroom Training Classes. Kubernetes Online Training. You get the best course at an affordable cost. Call on - +91-9989971070.
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Visualpath is the best DevOps Online Training Institute in Hyderabad. We are providing Online Training classes by real-time faculty with real time Projects. DevOps Training Online. You will get the best course at an affordable cost. Call on - +91-9989971070.
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Visualpath is the best DevOps Online Training Institute in Hyderabad. We are providing Online Training classes by real-time faculty with real time Projects. DevOps Training Online. You will get the best course at an affordable cost. Call on - +91-9989971070.
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Visualpath is the Leading and Best Azure DevOps Training institute in Ameerpet. We are Providing Live Instructor Led Online Training Classes delivered by experts from the industry using Real-time projects. Azure DevOps Online Course. You get the best course at an affordable cost. Call on - +91-9989971070.
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Find out more about ISO training and certification services
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For more information about PECB:
Website: https://pecb.com/
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Facebook: https://www.facebook.com/PECBInternational/
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- 1. GCP Data Engineer Roles +91-9989971070 www.visualpath.in
- 2. • A "GCP Data Engineer" is a professional who specializes in designing, building, and maintaining data pipelines and infrastructure on Google Cloud. They are responsible for creating solutions that collect, process, and analyze data to provide valuable insights to the organization. www.visualpath.in
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- 8. CONTACT For More Information About GCP Data Engineer Online Training Address:- Flat no: 205, 2nd Floor, Nilgiri Block, Aditya Enclave, Ameerpet, Hyderabad-16 Ph No : +91-9989971070 Visit : www.visualpath.in E-Mail : online@visualpath.in