Skip to main content

Features not available with Oracle Standard Edition

1. Oracle Data Guard—Redo Apply
2. Oracle Data Guard—SQL Apply
3. Oracle Data Guard—Snapshot Standby
4. Oracle Active Data Guard
5. Rolling Upgrades—Patch Set, Database, and Operating System
6. Online index rebuild
7. Online index-organized table organization
8. Online table redefinition
9. Duplexed backup sets
10. Block change tracking for fast incremental backup
11. Unused block compression in backups
12. Block-level media recovery
13. Lost Write Protection
14. Parallel backup and recovery
15. Tablespace point-in-time recovery
16. Trial recovery
17. Fast-start fault recovery
18. Flashback Table
19. Flashback Database
20. Flashback Transaction
21. Flashback Transaction Query
22. Oracle Total Recall
23. Client Side Query Cache
24. Query Results Cache
25. PL/SQL Function Result Cache
26. In-Memory Database Cache
27. SQL Plan Management
28. Support for Oracle Exadata Storage Server
29. Support for Oracle Exadata Storage Server Software
30. Advanced Security Option
31. Oracle Label Security
32. Virtual Private Database
33. Fine-grained auditing
34. Oracle Database Vault
35. Secure External Password Store
36. Oracle Change Management Pack
37. Oracle Configuration Management Pack
38. Oracle Diagnostic Pack
39. Oracle Tuning Pack
40. Oracle Provisioning and Patch Automation Pack
41. Database Resource Manager
42. Oracle Real Application Testing
43. Oracle Partitioning
44. Oracle OLAP
45. Oracle Data Mining
46. Oracle Advanced Compression
47. Direct-Load Table Compression
48. Bitmapped index, bitmapped join index, and bitmap plan conversions
49. Parallel query/DML
50. Parallel statistics gathering
51. Parallel index build/scans
52. Parallel Data Pump Export/Import
53. Transportable tablespaces, including cross-platform
54. Summary management—Materialized View Query Rewrite
55. Asynchronous Change Data Capture
56. Advanced Replication
57. Oracle Connection Manager
58. Infiniband Support
59. Oracle Spatial
60. Semantic Technologies (RDF/OWL)
Labels:

Comments

Post a Comment

Popular posts from this blog

What is the difference between Elastic and Enterprise Redis w.r.t "Hybrid Query" capabilities

  We'll explore scenarios involving nested queries, aggregations, custom scoring, and hybrid queries that combine multiple search criteria. 1. Nested Queries ElasticSearch Example: ElasticSearch supports nested documents, which allows for querying on nested fields with complex conditions. Query: Find products where the product has a review with a rating of 5 and the review text contains "excellent". { "query": { "nested": { "path": "reviews", "query": { "bool": { "must": [ { "match": { "reviews.rating": 5 } }, { "match": { "reviews.text": "excellent" } } ] } } } } } Redis Limitation: Redis does not support nested documents natively. While you can store nested structures in JSON documents using the RedisJSON module, querying these nested structures with complex condi...
Post a Comment
Read more

How are vector databases used?

  Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.
Post a Comment
Read more

Feature Engineering - What and Why

Feature engineering is a crucial step in the machine learning pipeline where you create new, meaningful features or transform existing features to improve the performance of your predictive models. It involves selecting, modifying, or creating features from your raw data to make it more suitable for machine learning algorithms. Here's a more detailed overview of feature engineering: Why Feature Engineering? Feature engineering is essential for several reasons: Improving Model Performance: Well-engineered features can significantly boost the predictive power of your machine learning models. Handling Raw Data: Raw data often contains noise, missing values, and irrelevant information. Feature engineering helps in cleaning and preparing the data for analysis. Capturing Domain Knowledge: Domain-specific insights can be incorporated into feature creation to make the model more representative of the problem. Common Techniques and Strategies: 1. Feature Extraction: Transforming raw data...
Post a Comment
Read more