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ADB Newsletter for April 2023

명품관 2023.04.11 11:12 조회 수 : 8864

ADB Newsletter for April 2023

 

URL : https://blogs.oracle.com/datawarehousing/post/adb-newsletter-for-april

 

Autonomous Database 뉴스레터입니다.

2023년 3월에 다뤄진 내용들에 대한 뉴스레터입니다.

 

관심 있으신 분은 하나씩 살펴 보시면 도움될 거 같습니다.

 

이런 기능들이 추가된 소식이 있나보네요

 

Simpler Management

 

Cloud Links

Backup-based disaster recovery, 

Real Application Testing - Capture and Replay, 

Availability domain information, 

Export data in Parquet format

 

Richer Analytics

 

Oracle Machine Learning - data and model monitoring

PL/SQL geocoding AP

 

추가로 몇가지 내용이 더 있는 거 같습니다.

아래는 원문 내용입니다.

참고하세요


ADB Newsletter for April 2023

April 6, 2023 | 6 minute read
Keith Laker
Senior Principal Product Manager

Here are the latest updates delivered during March for #autonomousdatabase:

Simpler Management

  • Cloud Links
  • Backup-based disaster recovery, 
  • Real Application Testing - Capture and Replay, 
  • Availability domain information, 
  • Export data in Parquet format

Richer Analytics

  • Oracle Machine Learning - data and model monitoring
  • PL/SQL geocoding AP

See below for more information on each of these topics. Feel free to contact me via email (keith.laker@oracle.com) if you have any questions about these new features.


CLOUD LINK

Cloud Links allow a data owner to quickly and easily register and share a table or view for remote access by users of other Autonomous Databases. 

This makes it possible to discover data without going through the complex process of defining a database link. Autonomous Database provides transparent access using SQL and implements privilege enforcement.

BUSINESS BENEFIT

Cloud Links greatly simplify the sharing of tables or views across Autonomous Database instances compared to traditional database linking mechanisms. ADB is now a powerful solution where data collaboration is a crucial requirement. 

LEARN MORE 

  • DOC: Using Cloud Links for read-only data access on Autonomous Database, click here 
  • BLOG: Database links in Autonomous Database Shared are the past - Cloud links are the future, click here  

BACKUP-BASED DISASTER RECOVERY

Backup-based disaster recovery uses backups to create a peer database during switchover or failover. For local backup-based disaster recovery, existing local backups are utilized. There are no additional costs for a local Backup-Based Disaster Recovery. 

An additional cost is incurred for cross-region backup-based DR. 

For better resilience, a local peer is instantiated in regions with more than one availability domain in a different availability domain than the primary database. In regions with a single availability domain, a local backup-based peer is instantiated in a different fault domain than the primary database (that is, on another physical machine).

BUSINESS BENEFIT

Provides customers with a much lower cost disaster recovery option for Autonomous Database compared to Autonomous Data Guard. Obviously, there is a correspondingly higher Recovery Time Objective.

LEARN MORE 

  • DOC: Using Backup-Based Disaster Recovery, click here 
  • BLOG: Introducing Backup-Based Disaster Recovery: A low-cost DR option for databases with higher RTO tolerance, click here 

REAL APPLICATION TESTING

It is now possible to use Real Application Testing to capture a workload on one Autonomous Database instance and then replay that workload on another Autonomous Database instance. This makes it easy to compare running a workload across different Autonomous Database instances. These Autonomous Database instances may vary at patch levels or database versions. 

BUSINESS BENEFIT

This highly cost-effective and easy-to-use proactive performance management solution enables businesses to fully assess the outcome of any changes in a controlled, repeatable way.

LEARN MORE 

  • DOC: Using Oracle Real Application Testing, click here 

AVAILABILITY DOMAIN INFORMATION

This is useful when considering where to deploy application servers when an Autonomous Database instance resides in a multi-availability domain region. To reduce latency as much as possible, deploying the application server and database in the same availability domain is essential. 

BUSINESS BENEFIT

This makes it easier to optimize overall application performance through the co-location of application servers and database instances.

LEARN MORE 


EXPORT DATA IN PARQUET FORMAT

This export method supports all the cloud object stores supported by Autonomous Database and Oracle Cloud Infrastructure resource principal to access your Oracle Cloud Infrastructure Object Store, Amazon Resource Names (ARNs) to access AWS Simple Storage Service (S3), an Azure service principal to access Azure BLOB storage, or a Google service account to access Google Cloud Platform (GCP) resources. 

WHAT IS PARQUET?

Apache Parquet is an open-source, column-oriented data file format designed for efficient data storage and retrieval. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk. Apache Parquet is designed to be a common interchange format for both batch and interactive workloads. 

BUSINESS BENEFIT

Using Parquet provides significant benefits for data lake, data warehouse, and app development projects: 

  • Good for storing any data (structured data tables, images, videos, documents).
  • Saves on cloud storage space using highly efficient column-wise compression and flexible encoding schemes for columns with different data types.
  • Increased data throughput and performance using techniques like data skipping, whereby queries that fetch specific column values need not read the entire row of data.

LEARN MORE 

  • DOC: Export Data as Parquet to Cloud Object Storage, click here 
  • DOC: DBMS_CLOUD Package Oracle Data Type to Parquet Mapping, click here 
  • BLOG: How to Export Data in Parquet format from Autonomous Database tables to your cloud object store, click here

ORACLE MACHINE LEARNING DATA AND MODEL MONITORING

Oracle Machine Learning (OML) Monitoring - a new feature of OML Services on Autonomous Database - alerts users to issues in both data and in-database ML model quality and enables comparing changes relative to a baseline dataset. In data-driven enterprises, you need to know if there are significant changes in your data over time, as well as whether machine learning models built from your data are performing as expected.

DATA MONITORING

OML data monitoring enables comparing and tracking data changes over time. It can quickly and reliably identify changes in the characteristics of the underlying data enabling data stewards to take corrective action. Data monitoring aids in maintaining organizational data quality standards to help ensure the integrity of enterprise applications and dashboards. 

MODEL MONITORING

Model monitoring includes identifying when the user-specified model metric changes significantly or the distribution of predicted values deviates too much from initial values. Such deviations can signify the need to rebuild models.

OML Services supports model management, deployment, and monitoring while benefiting from system-provided infrastructure and an integrated database architecture. The model management and deployment services enable users to deploy in-database machine learning models to ADB. 

OML Services also enables deploying third-party models exported in ONNX format, which may have been produced separately from packages such as Tensorflow, PyTorch, scikit-learn, among others. 

BUSINESS BENEFIT

OML Services is included with Oracle Autonomous Database - so no additional licenses are required and no additional, separate services to manage. 

Unlike other machine learning solutions that require provisioning separate VMs for 24 - 7 availability, customers using OML Services pay only the additional compute when producing actual predictions. OML Services benefits from Autonomous Database's highly available architecture. 

LEARN MORE 

  • EXAMPLES: Notebooks and associated Postman Collection in GitHub, click here 
  • BLOG: Announcing OML Monitoring on Autonomous Database, click here 
  • DOC: Work with Data Monitoring, click here 
  • DOC: Work with Model Monitoring, click here

IN-DATABASE PL/SQL GEOCODING API

Maps are the standard way of visualizing location information. Plotting an address on a map requires converting the address to a pair of geographic coordinates. The conversion process is known as geocoding and this ability has been an integral part of Spatial Studio for years. 

Autonomous Database now includes a built-in PL/SQL package that enables geocoding directly within the database, which removes the need to install or maintain reference data or create callouts to external location-based services.

BUSINESS BENEFIT

The in-database geocoding, which is part of Autonomous Database, removes the cost, complexity, and latency of managing additional reference location data and/or using an additional external location-based service. 

This makes it easier and more cost-effective to deliver geocoded capabilities and use location-based analytics across a broad range of use cases. 

LEARN MORE 

  • BLOG: Now available: Geocoding API in Oracle Autonomous Database (Shared), click here 
  • DOC: Geocoding Address Data, click here 
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