{"id":4652,"date":"2020-04-13T17:04:56","date_gmt":"2020-04-13T17:04:56","guid":{"rendered":"https:\/\/www.testpreptraining.com\/tutorial\/?page_id=4652"},"modified":"2020-04-17T19:12:42","modified_gmt":"2020-04-17T19:12:42","slug":"process-and-analyze-google-professional-data-engineer-gcp","status":"publish","type":"page","link":"https:\/\/www.testpreptraining.ai\/tutorial\/process-and-analyze-google-professional-data-engineer-gcp\/","title":{"rendered":"Process and analyze Google Professional Data Engineer GCP"},"content":{"rendered":"<ul>\n<li>Processing and transformation is needed<\/li>\n<li>The processing framework can analyze data directly or prepare the data for downstream analysis<\/li>\n<li>Steps usually involved<\/li>\n<li>Processing: Data from source is cleansed, normalized, and processed<\/li>\n<li>Analysis: Processed data is stored for querying and exploration.<\/li>\n<li>Understanding: Based on analytical results, data is used to train and test automated machine-learning models.<\/li>\n<\/ul>\n<h3><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-4670\" src=\"https:\/\/www.testpreptraining.ai\/tutorial\/wp-content\/uploads\/2020\/04\/Professional-Data-Engineer-Google-Cloud-image011.png\" alt=\"\" width=\"590\" height=\"203\" \/><\/h3>\n<h3>Processing large-scale data<\/h3>\n<ul>\n<li>Large-scale data processing with data source from Cloud Storage, Bigtable, or Cloud SQL<\/li>\n<li>For large to fit data frameworks manage distributed compute clusters<\/li>\n<\/ul>\n<p>Dataproc<\/p>\n<ul>\n<li>A managed Apache Hadoop and Apache Spark service<\/li>\n<li>Hadoop and Spark are popular and supported by Dataproc<\/li>\n<li>No infrastructure needed to run Hadoop or Spark jobs in Dataproc<\/li>\n<li>Migrate Hadoop or Spark deployments to Dataproc<\/li>\n<li>Dataproc automates cluster creation, simplifies configuration and management of cluster<\/li>\n<li>Has built-in monitoring and utilization reports<\/li>\n<li>Use cases.\n<ul>\n<li>Log processing<\/li>\n<li>Reporting<\/li>\n<li>On-demand Spark clusters<\/li>\n<li>Machine learning<\/li>\n<\/ul>\n<\/li>\n<li>Simplifies operational activities like installing software or resizing a cluster.<\/li>\n<li>Also access, read data and write results in Cloud Storage, Bigtable, or BigQuery, or HDFS Can store and checkpoint data externally<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>Dataflow<\/p>\n<ul>\n<li>A serverless, fully managed batch and stream processing service<\/li>\n<li>Designed to simplify big data for both streaming and batch workloads.<\/li>\n<li>No need to specify a cluster size and manage capacity,<\/li>\n<li>Dataflow has on-demand resources are created, autoscaled, and parallelized.<\/li>\n<li>A true <em>zero-ops<\/em> service<\/li>\n<li>straggler workers addressed by constant monitoring, identifying, and rescheduling work, including splits, to idle workers across the cluster.<\/li>\n<li>Use-cases.\n<ul>\n<li>MapReduce replacement<\/li>\n<li>User analytics<\/li>\n<li>Data science<\/li>\n<li>ETL<\/li>\n<li>Log processing<\/li>\n<\/ul>\n<\/li>\n<li>The Dataflow SDK released as Apache Beam<\/li>\n<li>Supports execution on Apache Spark and Apache Flink.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>Dataprep by Trifacta<\/p>\n<ul>\n<li>A visual data exploration, cleaning, and processing service<\/li>\n<li>A service for visually exploring, cleaning, and preparing data for analysis.<\/li>\n<li>Use Dataprep by a browser based-UI, without coding.<\/li>\n<li>Automatically deploys and manages the resources required.<\/li>\n<li>Transform data in CSV, JSON, or relational-table formats.<\/li>\n<li>It uses Dataflow to scale automatically and can handle terabyte-scale datasets.<\/li>\n<li>Can export clean data to BigQuery<\/li>\n<li>Manage user access and data security with Cloud IAM<\/li>\n<li>Use cases\n<ul>\n<li>Machine Learning: You can clean training data for fine-tuning ML models.<\/li>\n<li>Analytics: You can transform raw data so that it can be ingested into data warehousing tools such as BigQuery.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Analyzing and querying data<\/h3>\n<p>For data analysis and query,\u00a0\u00a0 tools are listed.<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>BigQuery<\/p>\n<ul>\n<li>A managed data warehouse service<\/li>\n<li>Supports ad-hoc SQL queries and complex schemas.<\/li>\n<li>Use standard SQL queries or BI and visualization tools<\/li>\n<li>It is a highly-scalable, highly-distributed, low-cost analytics OLAP data warehouse<\/li>\n<li>Can have a scan rate of over 1 TB\/sec.<\/li>\n<li>To start, create a dataset in project, load data into a table, and execute a query.<\/li>\n<li>Data loading from Pub\/Sub, Dataflow, Cloud Storage, or output from Dataflow or Dataproc. Can also import CSV, Avro and JSON data formats<\/li>\n<li>Supports nested and repeated items in JSON.<\/li>\n<li>All data accessed (at rest or in motion) are encrypted.<\/li>\n<li>Covered under Google&#8217;s compliance programs<\/li>\n<li>Access to data controlled by ACLs.<\/li>\n<li>Billing based on: queries and storage.<\/li>\n<li>Querying data under pricing models: on-demand or flat-rate.\n<ul>\n<li>on-demand pricing &#8211; charges as per terabytes processed.<\/li>\n<li>flat-rate pricing &#8211; consistent query capacity with a simpler cost model.<\/li>\n<\/ul>\n<\/li>\n<li>BigQuery automates infrastructure maintenance windows and data vacuuming.<\/li>\n<li>query plan explanation is used in design of queries<\/li>\n<li>Data stored in a columnar format, optimized for large-scale aggregations and data processing.<\/li>\n<li>Built-in support for time-series partitioning of data.<\/li>\n<li>Run queries by standard SQL, with support for querying nested and repeated data.<\/li>\n<li>built-in functions and operators also provided.<\/li>\n<li>Use cases\n<ul>\n<li>User analysis<\/li>\n<li>Device and operational metrics<\/li>\n<li>Business intelligence<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Understanding data with machine learning<\/h3>\n<ul>\n<li>Machine learning is now crucial in analysis phase of the data lifecycle.<\/li>\n<li>Used to augment processed results<\/li>\n<li>suggest data-collection optimizations<\/li>\n<li>predict outcomes in data sets.<\/li>\n<li>Use cases.\n<ul>\n<li>Product recommendations<\/li>\n<li>Prediction<\/li>\n<li>Automated assistants<\/li>\n<li>Sentiment analysis<\/li>\n<\/ul>\n<\/li>\n<li>ML Options\n<ul>\n<li>Task-specific machine learning APIs<\/li>\n<li>Custom machine learning<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>The Vision API<\/p>\n<ul>\n<li>Used to analyze and understand the content of an image<\/li>\n<li>Uses pre-trained neural networks.<\/li>\n<li>Can classify images, detect individual objects and faces, and recognize printed words.<\/li>\n<li>Can detect inappropriate content.<\/li>\n<li>API is accessible through REST endpoints.<\/li>\n<li>Send images directly or upload to Cloud Storage and send link to image in the request. Requests can have single or multiple images<\/li>\n<li>Feature annotations can be selected<\/li>\n<li>Feature detection includes labels, text, faces, landmarks, logos, safe search, and image properties (like dominant colors).<\/li>\n<li>Response has metadata about each feature type annotation selected for each supplied in the original request.<\/li>\n<li>Integrate with other GCP services<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>Speech-to-Text<\/p>\n<ul>\n<li>Used to analyze audio and convert it to text.<\/li>\n<li>Recognizes more than 80 languages and variants<\/li>\n<li>Powered by deep-learning neural-network algorithms which constantly evolve and improve.<\/li>\n<li>Use cases\n<ul>\n<li>Real-time speech-to-text<\/li>\n<li>Batch analysis<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>Natural Language API<\/p>\n<ul>\n<li>Used to analyze and reveal the structure and meaning of text.<\/li>\n<li>Can extract information about people, places, events, sentiment of the input text, etc.<\/li>\n<li>Results used to filter inappropriate content, classify content by topics, or build relationships<\/li>\n<li>Combine with the Vision API for OCR capabilities or the Speech-to-Text features<\/li>\n<li>API is available through REST endpoints.<\/li>\n<li>Send text directly to the service, or upload to Cloud Storage and send text file link in request.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>Cloud Translation<\/p>\n<ul>\n<li>To translate more than 90 different languages.<\/li>\n<li>It automatically detects the input language, with high accuracy.<\/li>\n<li>Provide real-time translation for web and mobile apps<\/li>\n<li>supports batched requests for analytical workloads.<\/li>\n<li>Available through REST endpoints.<\/li>\n<li>Integrate the API with GCP services<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>Video Intelligence API: Video search and discover<\/p>\n<ul>\n<li>Used to search, discover, and extract metadata from videos.<\/li>\n<li>Has an easy-to-use REST API<\/li>\n<li>Can detect entities (nouns) in video content<\/li>\n<li>Also look for entities in scenes in the video content.<\/li>\n<li>Can annotate videos with frame-level and video-level metadata.<\/li>\n<li>Supports common video formats, like MOV, MPEG4, MP4, and AVI.<\/li>\n<li>Create a JSON request file with the location of the video and annotation types to perform, and submit to the API endpoint.<\/li>\n<li>Use cases\n<ul>\n<li>Extract insights from videos<\/li>\n<li>Video catalog search<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>AI Platform<\/p>\n<ul>\n<li>A managed machine learning platform<\/li>\n<li>Used to run custom machine learning models at scale.<\/li>\n<li>Create models using the TensorFlow framework, and use AI Platform to manage preprocessing, training, and prediction.<\/li>\n<li>It is integrated with Dataflow for data pre-processing<\/li>\n<li>Can access data stored in Cloud Storage or BigQuery.<\/li>\n<li>Also works with Cloud Load Balancing<\/li>\n<li>Develop and test TensorFlow models<\/li>\n<li>Uses Datalab and Jupyter notebooks<\/li>\n<li>Models are completely portable.<\/li>\n<li>AI Platform workflow\n<ul>\n<li>Preprocessing: Involves training, evaluation, and test data<\/li>\n<li>Graph building: convert supplied TensorFlow model into an AI Platform model with operations for training, evaluation, and prediction.<\/li>\n<li>Training: Continuously iterates and evaluates the model according to submitted parameters.<\/li>\n<li>Prediction: Use the model to perform computations in batches or on-demand<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<p>General-purpose machine learning<\/p>\n<ul>\n<li>Can deploy other high-scale ML tools like Mahout and MLlib<\/li>\n<li>Offers ML algorithms for clustering, classification, collaborative filtering, and more.<\/li>\n<li>Use Dataproc to deploy managed Hadoop and Spark clusters,<\/li>\n<li>Run and scale ML workloads<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Processing and transformation is needed The processing framework can analyze data directly or prepare the data for downstream analysis Steps usually involved Processing: Data from source is cleansed, normalized, and processed Analysis: Processed data is stored for querying and exploration. Understanding: Based on analytical results, data is used to train and test automated machine-learning models&#8230;.<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"categories":[617],"tags":[619,644,623,622,618,647,621],"class_list":["post-4652","page","type-page","status-publish","hentry","category-google-gcp","tag-data-engineer","tag-data-lifecycle","tag-gcp","tag-google-certification","tag-google-cloud","tag-process-and-analyze-phase","tag-professional-data-engineer"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Process and analyze Google Professional Data Engineer GCP - Testprep Training Tutorials<\/title>\n<meta name=\"description\" content=\"Google Cloud Certified Professional Data Engineer Tutorial, dumps, brief notes on Process and analyze\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.testpreptraining.ai\/tutorial\/process-and-analyze-google-professional-data-engineer-gcp\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Process and analyze Google Professional Data Engineer GCP - 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