Store Google Professional Data Engineer GCP

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Data is of various types as

Object Storage

Tools for object storage are listed.

Cloud Storage

  • A managed object storage service
  • Durable and highly-available storage for structured and unstructured data
  • Can store
    • log files
    • database backup
    • export files
    • images
    • binary files.
  • Files organized by project into individual buckets.
  • Buckets can support either custom ACLs or IAM controls.
  • Logging by Cloud Logging.
  • Use cases
    • Data backup and disaster recovery
    • Content distribution – store and deliver media files
    • Storing ETL data
    • Storing data for MapReduce jobs
    • Storing query data
    • Seeding machine learning
    • Archiving cold data
  • Multiple storage classes offered
    • Standard Storage has highest availability, low-latency access for frequently accessed data, like serving website content, interactive storage workloads, data supporting mobile and gaming apps, data-intensive computations and big data processing.
    • Nearline Storage is low-cost, highly durable storage if data is accessed once a month. Gives sub-second response times and apt for data archiving, online backup, or disaster recovery.
    • Coldline Storage is a very-low-cost, highly durable storage for one a quarter data access. Gives sub-second response times, and apt for data archiving, online backup, and disaster recovery.
    • Archive Storage is lowest-cost, highly durable storage for once a year data access. Gives fast access with sub-second response times and suitable for data archiving, online backup, and disaster recovery.

Cloud Storage for Firebase

  • Scalable storage service for mobile app developers
  • Designed to scale with user base.
  • Also good for storing and retrieving assets such as images, audio, video, and other user-generated content in mobile and web apps.
  • Firebase SDKs for uploads and downloads
  • It stores files in a Cloud Storage bucket,
  • Can do server-side processing like image filtering or video transcoding


Storing database data

Tools for databases, both RDBMS and NoSQL, are listed.

Cloud SQL

  • A managed service giving MySQL and PostgreSQL engine
  • built-in support for replication
  • Provides low-latency, transactional and relational database workloads
  • Supports standard APIs for connectivity.
  • Has built-in backup and restoration, high availability, and read replicas.
  • Supports RDBMS workloads up to 30 TB for both MySQL and PostgreSQL.
  • Accessible from apps running on App Engine, GKE, or Compute Engine.
  • Also supports standard connection drivers and app frameworks (like Django, Ruby on Rails) Data stored is encrypted in transit and at rest.
  • Also has built-in support for access control, using network firewalls.
  • Use cases for Cloud SQL OLTP
    • Financial transactions
    • User credentials
    • Customer orders
  • Also suitable for OLAP workloads or data needing dynamic schemas on a per-object basis.
  • For dynamic schemas, use Datastore and for OLAP use BigQuery and for wide-column schemas, use Bigtable. Use Dataflow or Dataproc for ETL



  • A managed service for wide-column NoSQL
  • Designed for terabyte- to petabyte-scale workloads.
  • Built on Google’s internal Bigtable database infrastructure
  • Provides consistent, low-latency, and high-throughput storage for large-scale NoSQL data. Supports real-time app serving and large-scale analytical workloads.
  • Use a single-indexed row key associated with a series of columns
  • queries are based on row key
  • Schemas are structured as tall or wide
  • The style of schema is dependent on the downstream use cases and it’s important to consider data locality and distribution of reads and writes to maximize performance.
  • Tall schemas used for time-series events, as data is keyed by a timestamp, with relatively fewer columns per row.
  • Wide schemas, a simplistic identifier as the row key along with a large number of columns.
  • Use cases
    • Real-time app data
    • Stream processing
    • IoT time series data
    • Adtech workloads
    • Data ingestion
    • Analytical workloads
    • Apache HBase replacement
  • No support for multi-row transactions, SQL queries or joins.



  • A horizontally scalable relational database service
  • Has strong consistency, high availability, and global scale.
  • Has ease of use and familiarity of a RDBMS with the scalability of a NoSQL database.
  • Spanner supports
    • Schemas
    • ACID transactions
    • SQL queries (ANSI 2011)
  • Scales horizontally in regions and can scale across regions
  • Perform automatic sharding and give millisecond latencies.
  • Security includes data-layer encryption, audit logging, and Cloud IAM integration.
  • Use cases
    • Financial services
    • Ad tech
    • Retail and global supply chain



  • A flexible, scalable NoSQL database service
  • stores JSON data
  • JSON data can be synchronized in real time to connected clients
  • Firestore API lets app persist data to a local disk
  • Has a flexible, expression-based rules language
  • Firestore Security Rules for authentication
  • Use cases
    • Chat and social media
    • Mobile games


Ecosystem databases

  • Can deploy own database software on Compute Engine VMs
  • Traditional RDBMS supported like EnterpriseDB and Microsoft SQL Server
  • NoSQL database systems like MongoDB and Cassandra


Storing data warehouse data

A data warehouse stores large quantities of data for query and analysis instead of transactional processing. For data-warehouse workloads, Google Cloud provides BigQuery.



  • A managed data warehouse service
  • Supports ingestion by web interface, command line tools, and REST API calls.
  • Bulk loading in CSV, JSON, or Avro files.
  • For streaming data, use Pub/Sub and Dataflow
  • Can also stream data directly into BigQuery