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Automatic Indexing (AI) is probably the most important new feature of Oracle Database 19c and AI is arguably one of the best example of AI in the IT industry. But there is much more that came along with 19c.

Here is my choice of the top 10 least known (for now at least) new features of Oracle Database 19.2 (in no order whatsoever):

1. Security: if the database password file name or location has been changed and the metadata cache needs to be refreshed with the details of the updated database password file, you can do it simply with the following command:

SQL> alter system flush passwordfile_metadata_cache;

System altered.

In 19c, most of the Oracle Database supplied schema-only accounts now have their passwords removed to prevent users from authenticating to these accounts. But DBAs can still assign passwords to the default schema-only accounts.

2. SQL: the LISTAGG aggregate function now supports duplicate elimination by using the new DISTINCT keyword.

There are also a new bitvector SQL operators can be used to speed up COUNT DISTINCT operations within a SQL query.

3. Active Data Guard DML redirection: You can now run DML on the Active Data Guard standby database. When run on the standby side, the update is passed to the Primary database where it is executed and the resulting redo of the transaction will update the standby after which control will be returned to the application.

4. There are 6 new init.ora parameters in 19c. DBAs can now tune in DataGuard the amount of wait time by using DATA_GUARD_MAX_IO_TIME and DATA_GUARD_MAX_LONGIO_TIME. You can check details for all six:

ADG_REDIRECT_DML
DATA_GUARD_MAX_IO_TIME
DATA_GUARD_MAX_LONGIO_TIME
ENABLE_IMC_WITH_MIRA
LOB_SIGNATURE_ENABLE
MAX_DATAPUMP_PARALLEL_PER_JOB

5. You can now create Materialized Views containing bitmap-based COUNT(DISTINCT) functions. This means that the MVs are based on SQL aggregate functions that use bitmap representation to express the computation of COUNT(DISTINCT) operations.

SQL> create materialized view MV_SALES as
select CLIENT, 
BITMAP_CONSTRUCT_AGG(BITMAP_BIT_POSITION(PRICE),'RAW') bm_price
from SALES
group by CLIENT,BITMAP_BUCKET_NUMBER(PRICE);  

Materialized view created.

6. Looks like there is now automatic resolution of SQL plan regressions: “SQL plan management searches for SQL statements in the Automatic Workload Repository (AWR). Prioritizing by highest load, it looks for alternative plans in all available sources, adding better-performing plans to the SQL plan baseline. Oracle Database also provides a plan comparison facility and improved hint reporting.”

7. Real-Time Statistics is also a new cool feature. “Oracle automatically gathers online statistics during conventional DML operations. Statistics can go stale between execution of DBMS_STATS statistics gathering jobs. By gathering some statistics automatically during DML operations, the database augments the statistics gathered by DBMS_STATS. Fresh statistics enable the optimizer to produce more optimal plans.”

8. Hybrid Partitioned Tables: now large portions of a table can reside in external partitions, for example in the Oracle Cloud. With this new feature, you can also easily move non-active partitions to external files, such as Oracle Data Pump files, for a cheaper storage solution. Hybrid partitioned tables support all existing external table types for external partitions: ORACLE_DATAPUMP, ORACLE_LOADER, ORACLE_HDFS, ORACLE_HIVE.

9. Data Pump:

– Oracle Data Pump allows tablespaces to stay read-only during TTS import.
– Oracle Data Pump can work in test mode for transportable tablespaces.
– Oracle Data Pump supports resource usage limitations with the introduction of two new parameters: MAX_DATAPUMP_JOBS_PER_PDB and MAX_DATAPUMP_PARALLEL_PER_JOB.
– Data Pump no longer enables secure, password-protected roles by default. Beginning with 19c, you must explicitly enable password-protected roles for an individual export or import job. A new command-line parameter has been added, ENABLE_SECURE_ROLES=YES | NO that can be used to explicitly enable or disable these types of roles for an individual export or import job.
– The new Data Pump command-line parameter CREDENTIAL enables secure import into a managed service from dump files in the Oracle Object Store Service.

10. SQL Quarantine: “SQL statements that are terminated by Oracle Database Resource Manager due to their excessive consumption of CPU and I/O resources can be automatically quarantined. The execution plans associated with the terminated SQL statements are quarantined to prevent them from being executed again.”

Check out the new Oracle package DBMS_SQLQ – cool stuff!

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“By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.” — Eliezer Yudkowsky

CNBC: 40% of A.I. start-ups in Europe have almost nothing to do with A.I.

Oracle 19c brings one key feature which does not exist in *any* other database system: Automatic Indexing.

For a very long time, both DBAs and Developers, have been struggling (really struggling) with what indexes should be created, what type of indexes they should be created as and what indexes should be dropped from the database. Automatic Index creation (AI Creation) means the explicit creation of new indexes and also dropping existing, unused indexes without human intervention.

In the long run, this is to be arguably one of the most important features in the Oracle database. I have already covered the basics in a previous blog post entitled Automatic Indexing in 19c. The expert system works in the following way passing through the stages of identification, verification and decision making:

Based on the captured workload, Oracle’s expert system identifies the index candidates which are created first as UNUSABLE & INVISIBLE (metadata only):

Then, there is the verification process. Some indexes will become VALID (physical segments are created) but will still stay INVISIBLE to the optimizer.

Later, Oracle decides if some of these indexes can become VISIBLE and this happens based on how the performance increases and how these new indexes affect other activities in the database.

Look for possible error using this query:

select EX.execution_type, EX.execution_name,F.message
from DBA_ADVISOR_FINDINGS F, DBA_ADVISOR_EXECUTIONS EX 
WHERE F.EXECUTION_NAME = EX.EXECUTION_NAME AND F.TYPE = 'ERROR';

If you need a detailed report from (say) the last 30 days, here is how to obtain it:

spool report
select dbms_auto_index.report_activity(sysdate-30,null,'text','all','all') report from dual;
spool off

A sample report shows beside the index candidates, space used, fatal errors also the overall improvement factor and also the SQL statement improvement factor:

When using/implementing the feature, have in mind the following:

– AUTO_INDEX_MODE must be set in every PDB: even set on container level it is not cascading to the pluggable databases
– Manually created indexes are nor dropped by default, you need to set separately AUTO_INDEX_RETENTION_FOR_MANUAL
– Follow the expert system runs from CDB_AUTO_INDEX_EXECUTIONS
– Hint for an INVISIBLE VALID index (for example /*+ index(clients SYS_AI_64uvm6wb5168u) */): I have seen how the index becomes VISIBLE in a second (if really useful)

For more details, check the recent bog post 19c Auto Index: the dictionary views by Franck Pachot

Automatic Indexing is by far one of the best examples of Artificial Intelligence and Machine Learning in the IT Industry. Really! I still remember a 5TB Oracle database I used to administer (mission critical one, a 24×7 system) where the indexes were almost 4.5TB in size while the real data was like half a TB only.

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One of the most impressive new features of Oracle Database 19c is Automatic Indexing. Arguably, this is the most interesting innovation in the database world for a rather long time.

I remember some years ago when a DBA asked me at an Oracle conference: “Julian, why are half of the presentations at Oracle database conference only about performance tuning? Is the Oracle database performing that badly that people should tune it all the time?” Sigh…

With 19c and ADB (Oracle Autonomous Database), things look very different now, don’t they? Automatic Indexing provides what database systems need: continuous optimization of the database workload, stable & solid performance and almost no human interaction. Let me share some of my early experience with Automatic Indexing and where human interaction is needed.

For now (February 18th, 2019), Oracle 19c is only available on Exadata (Linux 7.4) and in order to enable Automatic Indexing you need to do the following:

EXEC DBMS_AUTO_INDEX.CONFIGURE('AUTO_INDEX_MODE','IMPLEMENT');

As you can guess from the picture above, the so called expert system of Automatic Indexing runs every 15th minute for as long as one hour. Note that I disabled the job from 4:43 till 5:56. The Resource Manager plan limits the task to 1 CPU only and the next run is skipped if the job has not been completed within the 15 minutes.

Here are details on how Automatic Indexing works but what is most important to remember is as follows:

– The auto index candidates are created as invisible auto indexes
– If the performance of SQL statements is not improved from the auto indexes, then the indexes are marked as unusable and the corresponding SQL statements are blacklisted
– Auto indexes cannot be used for any first time SQL run against the database
– Auto indexes are created as either single, concatenated indexes or function-based indexes and they all use advanced low compression
– The unused auto indexes are deleted after 373 days (can be changed)
– The unused non-auto indexes (manual indexes) are never deleted by the automatic indexing process but can be deleted automatically if needed

The Auto Indexing can be disabled at any time or can be set to set to reporting mode (new auto indexes as created asinvisible indexes, so that they cannot be used in SQL) with the following commands:

EXEC DBMS_AUTO_INDEX.CONFIGURE('AUTO_INDEX_MODE','OFF');
EXEC DBMS_AUTO_INDEX.CONFIGURE('AUTO_INDEX_MODE','REPORT ONLY');

Here is a way to ask Oracle to create new auto indexes in a separate tablespace called AUTO_INDEX_TS:

SQL> EXEC DBMS_AUTO_INDEX.CONFIGURE('AUTO_INDEX_DEFAULT_TABLESPACE','AUTO_INDEX_TS');

PL/SQL procedure successfully completed.

Elapsed: 00:00:00.01

You can easily check the configuration for Automatic Indexing for the root container and the PDBs from CDB_AUTO_INDEX_CONFIG;

If you need a report of what happened during the expert system activity (either last 3 days or during the last activity), here is a way to generate it:

set long 300000
select DBMS_AUTO_INDEX.REPORT_ACTIVITY(SYSTIMESTAMP-3,SYSTIMESTAMP,'TEXT','ALL','ALL') from dual;
select DBMS_AUTO_INDEX.REPORT_LAST_ACTIVITY('TEXT','ALL','ALL') from dual;

These are the most important views about Auto Indexing:

DBA_AUTO_INDEX_EXECUTIONS: history of execution of automatic indexing tasks
DBA_AUTO_INDEX_STATISTICS: statistics related to auto indexes
DBA_AUTO_INDEX_IND_ACTIONS: actions performed on auto indexes
DBA_AUTO_INDEX_SQL_ACTIONS: actions performed on SQL statements for verifying auto indexes
DBA_AUTO_INDEX_CONFIG: configuration settings related to auto indexes
DBA_AUTO_INDEX_VERIFICATIONS: stats about PLAN_HASH_VALUE, AUTO_INDEX_BUFFER_GETS, etc.

The new package DBMS_AUTO_INDEX can be used for 3 main things:

1. Configuration of the parameters related to Auto Indexing
2. Drop *all* the indexes except the ones used for constraints
3. Report the activity of the “expert system”:

Finally, here are some additional resources:

Automatic Indexing in Oracle Database 19c
Oracle Database 19c is now available!
Managing Auto Indexes

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Building a new data warehouse in the cloud or migrating an existing one to cloud requires careful consideration and the answer to the question “Which cloud should I use?” is often “It depends”.

An interesting comparison of system properties comparing Amazon Redshift vs. Oracle vs. Snowflake can be found on db-engines.com

There are several other options too: Azure SQL Data Warehouse, Presto, Google BigQuery, etc.

An interesting benchmark paper called “Data Warehouse Benchmark: Redshift, Snowflake, Azure, Presto and BigQuery” by Fivetran is worth reading!

Another comparison called Interactive Analytics: Redshift vs Snowflake vs BigQuery is already more than 2 years old but still interesting.

Recently, things have changed. Oracle’s Autonomous Data Warehouse Cloud has been in GA for almost 1 year (since March 2018). ADW is for enterprise loads and mission critical systems arguably the best solution right now.

Viscosity compared both Oracle Autonomous and Amazon Redshift. The result? Check it here: Amazon vs Oracle: Data Warehouse Services, How do They Compare?

In short, the conclusion of the research above is:

– Oracle’s ADW was able to achieve data retrieval at the lowest latencies, and achieved the highest volume of queries per hour. In terms of serial query execution and multi-user query throughput.
– Oracle’s ADW consistently outperformed Redshift by a factor of 4x in both sets of tests.

And do not ignore the db-engines ranking! Only one of the three is in the Top 10.

What is interesting to know on top of all papers above are these 10 differences or let us call them less known technical facts (in no order of importance) between Oracle Autonomous, Amazon Redshift and Snowflake:

1. Snowflake compute usage is billed on a per-second basis, with a minimum of 60 seconds. Amazon Redshift is based on PostgreSQL 8.0.2 and is built on top of technology from the MPP data warehousing company ParAccel. Oracle Autonomous Database is based on Exadata and 18c.

2. In Oracle Autonomous Cloud, you can provision up to 128 CPUs and 128TB directly from the cloud console but you can provision more if needed.

3. Snowflake manages all aspects of how data is stored in S3 including data organization, file sizes, structure, compression, and statistics.

4. The only things needed for BYOL in Oracle Autonomous Database are Multitenant and RAC (only when using more than sixteen OCPUs). The standby option (not yet available) will require Active Data Guard as well.

5. Snowflake does not disclose the information about processing power and memory. Oracle do disclose the information via internal views but you cannot directly define the SGA or PGA size.

6. Redshift is not built as a high-concurrency database with several concurrent running queries and AWS recommends that you execute no more than 15 queries at a time. The number of concurrent user connections that can be made to a cluster is 500.

7. Oracle ADW and ATP allow you to partition both indexes and tables. In Snowflake partitioning is handled internally. Amazon Redshift does not support tablespaces, table partitioning, inheritance, and even certain constraints. Amazon Redshift Spectrum supports table partitioning using the CREATE EXTERNAL TABLE command.

8. The maximum number of tables in Amazon Redshift is 9,900 for large and xlarge cluster node types and 20,000 for 8xlarge cluster node types. The limit includes temporary tables. An Oracle database does not have a limit for the number of tables.

9. Oracle automatically applies all security updates (and online!) to ensure data is not vulnerable to known attack vectors. Additional in-database features like Virtual Private Database and Data Redaction are also available.

10. There is no operation in Snowflake for collecting database statistics. It is handled by the engine. In Oracle, database statistics collection is allowed. Both Oracle Autonomous and Amazon Redshift monitor changes to your workload and automatically update statistics in the background.

Finally, here are official URLs of all three products:

Oracle Autonomous Database
Amazon Redshift
Snowflake Database

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First things first: there is nothing else. Let me explain why.

Both Forbes and the Wall Street Journal wrote about the top 5 industry early adopters of Autonomous Systems.

According to the article, “in the IT industry, the pioneering product is Oracle’s Autonomous Data Warehouse Cloud, a cloud-based database that configures, optimizes and patches itself with minimal human intervention. Oracle Executive Chairman and CTO Larry Ellison says the machine learning technology that underpins the company’s autonomous data warehouse, as well as autonomous integration, developer, mobile and other platform services that will follow, is as revolutionary as the internet.”

To make it clear, the new Autonomous Data Warehouse and the Autonomous Transaction Processing databases are not based on newly written software. It is the same Oracle database with a lot of automation and mathematical algorithms embedded into the original database software. Think of machine learning and computer intelligence.

If you are looking for something similar among other database brands – good luck! Finding all areas of Self-Securing, Self-Automation and Self-Repairing outside Oracle Autonomous Database Cloud is mission impossible. And here are the areas:

Four Areas of Self-Securing of Autonomous Databases:

1. Self-securing starts with the security of the Oracle Cloud infrastructure and database service. Security patches are automatically applied every quarter or as needed, narrowing the window of vulnerability. Patching includes the full stack: firmware, operating system [OS], clusterware, and database. There are no steps required from the customer side.

2. Oracle encrypt customer data everywhere: in motion, at rest, and in backups. The encryption keys are managed automatically, without requiring any customer intervention. And encryption cannot be turned off.

3. Administrator activity on Oracle Autonomous Data Warehouse Cloud is logged centrally and monitored for any abnormal activities. Oracle have enabled database auditing using predefined policies so that customers can view logs for any abnormal access: UNIFIED_AUDIT_TRAIL

4. Built upon Oracle Database Vault, unique to Oracle Cloud, operations personnel have privilege to do all administrative tasks without any ability to ever see any customer data.

Four Areas of Self-Automation of Autonomous Databases:

1. Self-Automation: automatic provisioning of pluggable databases and automatic scaling – PDB resource manager.

2. Automatic tuning: SQL Plan Management, Adaptive Plans, SQL Tuning Advisor – Automatic SQL Tuning, Storage Indexes, Automatic Storage Management, Automatic detection and correction of regressions due to plan changes, Automatically tune memory, process, sessions.

3. Automatic Fault Tolerant Failover: RAC and Data Guard. Automatically kill run-away transactions and SQL. Automatically kill inactive session.

4. Automatic Backup and Recovery: RMAN, Flashback.

Seven Areas of Self-Repairing of Autonomous Databases:

Both Maria Colgan and Franck Pachot wrote on the differences between ADW and ADP:

How does Autonomous Transaction Processing differ from the Autonomous Data Warehouse? by Maria Colgan

ATP vs ADW – the Autonomous Database lockdown profiles by Franck Pachot

But here are in short the four main areas of differences between ADW and ADP:

1. Settings and parameters:
– In ADW: the majority of the memory is allocated to the PGA – joins, aggregations in memory
– In ATP: the majority of the memory is allocated to the SGA – minimize I/O

For DBAs: ADW runs on 94 non-default parameters out of which 35 are underscore. In ATP, the corresponding numbers are 94 and 36. Not same 94 though! And these numbers may slightly vary.

2. Data formats:
– In ADW: data is stored in a columnar format as that’s the best format for analytics processing – ADW uses DBIM option features like in-memory columnar flash cache under the covers
– In ATP: data is stored in a row format

3. Statistics/CBO:
– In ADW: statistics are automatically maintained as part of bulk load and DBMS_CLOUD activities
– In ATP: statistics are automatically gathered when the volume of data changes significantly enough to make a difference to the statistics

4. Client services/connections:
– In ADW: only one service (LOW) automatically runs SQL statements serially, all is parallel
– In ATP: the PARALLEL service does no longer exist (as of 12.11.2018)

FAQ for Oracle Autonomous Database

In order to show the other side of the coin, here are two perspective from IBM and SAP point of view:

Oracle Autonomous Database – is it truly self-driving? by Danny Arnold

How Real is The Oracle Automated Database? by Shaun Snapp

But if you prefer more neutral reading check Oracle’s next chapter: The Autonomous Database and the DBA and Will Autonomous Database Entice Big Business To The Cloud?

Bottom line: if you need extreme high reliability, top-level security, 100% automation of DBA routine tasks and no funny surprises – start testing and using the Oracle Autonomous Database. Really!

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Years ago, I saw this quiz on dbaspport. Below, we have 2 PL/SQL blocks, have a look:


BEGIN
  WHILE sysdate = sysdate LOOP
    NULL;
  END LOOP;
END;
/

DECLARE
  x DATE;
BEGIN
  LOOP
    BEGIN
      SELECT null INTO x FROM dual WHERE sysdate = sysdate;
    EXCEPTION
      WHEN NO_DATA_FOUND THEN EXIT;
    END;
  END LOOP;
END;
/

Question is: what happens after you run them? Are the loops above both finite, both infinite or is it so that one of them is finite and the other one infinite?

If you cannot answer the question just run them in SQL*Plus, etc. Then, try to explain why – the reason for being finite or infinite.

I will update this blog post after month or so with the answer.

And I have just run this against ATP (~18c) but the output is same in previous versions too:

with SALES as (select /*+ materialize */ 0/0 from DUAL) 
select count(*) from SALES;

ORA-01476: divisor is equal to zero
01476. 00000 -  "divisor is equal to zero"
*Cause:    
*Action:

with SALES as (select /*+ inline */ 0/0 from DUAL) 
select count(*) from SALES;

  COUNT(*)
----------
         1
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Databases are very much like wine, cheese and trees: they get better as they age.

Amazon Aurora exists since 2015. The word aurora comes Latin, means dawn. The name was borne by the Roman mythological goddess of dawn and by the princess in the fairy tale Sleeping Beauty.

Both Amazon’s “dawn” Aurora and Oracle’s ATP are typical cloud OLTP systems.

The question is: what are their differences, which one is better and meant exactly for my needs?

Oracle ATP is based on Oracle’s database and Exadata, here are all the innovations adopted from both systems:

Amazon’s Aurora has 2 flavors: Amazon Aurora MySQL and Amazon Aurora PostgreSQL.

Amazon Aurora MySQL is compatible with MySQL 5.6 using the InnoDB storage engine. Certain MySQL features like the MyISAM storage engine are not available with Amazon Aurora. Amazon Aurora PostgreSQL is compatible with PostgreSQL 9.6. The storage layer is virtualized and sits on a proprietary virtualized storage system backed up by SSD. And you pay $0.20 per 1 million IO requests.

Oracle’s Autonomous database comes also in 2 flavors: Oracle ADW and Oracle ATP. Check Franck Pachot’s article ATP vs ADW – the Autonomous Database lockdown profiles to see the differences of both cloud databases.

In general, one can compare Oracle ADW with Amazon Redshift and Oracle ATP with Oracle ATP.

One way to compare is to look at the ranking provided by DB-Engines: Amazon Aurora vs. Oracle. No-brainer who the leader is: score of 1300 vs score of 5 in favor of Oracle.

Another interesting comparison comes from Amalgam Insights. Check how Oracle Autonomous Transaction Processing lowers barriers to entry for data-driven business. Check out the DBA labor cost involved: 5 times less in favor of Oracle ATP compared to Amazon! All the routine DBA tasks have been totally eliminated.

The message from them is very clear: “Oracle ATP could reduce the cost of cloud-based transactional database hosting by 65%. Companies seeking to build net-new transactional databases to support Internet of Things, messaging, and other new data-driven businesses should consider Oracle ATP and should do due diligence on Oracle Autonomous Database Cloud for reducing long-term Total Cost of Ownership.”

This month (August 2018), there was an interesting article by Den Howlett entitled Oracle introduces autonomous transaction processing database – pounds on AWS. Here are 2 interesting and probably correct statements/quotes from there:

1. It really is hard to get off an established database, even one that can be as expensive as Oracle can turn out to be.
2. Some of the very largest workloads will not go to the public cloud anytime soon. Maybe never which in internet years is after 2030.

As a kind of proof of how reliable and fast Oracle’s Autonomous Transaction Processing database is consider the following OLTP workload running non-stop in a balanced way without any major spikes and without a single queued statement!

No human labor, no human error, and no manual performance tuning!

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“Big Data wins games but Data Warehousing wins championships” says Michael Jordan. Data Scientists create the algorithm, but as Todd Goldman says, if there is no data engineer to put it into production for use by the business, does it have any value?

If you google for Amazon Redshift vs Oracle, you will find lots of articles on how to migrate Oracle to Redshift. Is it worth it? Perhaps in some cases before Oracle Autonomous Data Warehouse Cloud existed.

Now, things look quite different. “Oracle Autonomous Data Warehouse processes data 8-14 times faster than AWS Redshift. In addition, Autonomous Data Warehouse Cloud costs 5 to 8x less than AWS Redshift. Oracle performs in an hour what Redshift does in 10 hours.” At least according to Oracle Autonomous Data Warehouse Cloud white paper. And I have nothing but great experiences with ADWC. For the past half an year or so.

But, what are the major issues and problems reported by Redshift users?

One of the most common complaints involves how Amazon Redshift handles large updates. In particular, the process of moving massive data sets across the internet requires substantial bandwidth. While Redshift is set up for high performance with large data sets, “there have been some reports of less than optimal performance,” for the largest data sets. An article by Alan R. Earls entitled Amazon Redshift review reveals quirks, frustrations claims that reviewers want more from the big data service. So:

Why to migrate from Amazon Redshift to Autonomous Data Warehouse Cloud?

1. Amazon Redshift is ranked 2nd in Cloud Data Warehouse with 14 reviews vs Oracle Exadata which is ranked 1st in Data Warehouse with 55 reviews.

The top reviewer of Amazon Redshift writes “It processes petabytes of data and supports many file formats. Restoring huge snapshots takes too long”. The top reviewer of Oracle Exadata writes “Thanks to smart scans, the amount of data transferred from storage to database nodes significantly decreases”.

2. Oracle Autonomous dominates in features and capabilities:

DB-engines shows an excellent system properties comparison of Amazon Redshift vs. Oracle.

In addition, reading through these thoughts on using Amazon Redshift as a replacement for an Oracle Data Warehouse can be worthwhile. It shows how Amazon Redshift compares with a more traditional DW approach. But Enterprises have some Redshift concerns, including:

– The difference between versions of PostgreSQL and the version Amazon uses with Redshift
– The scalability of very large data volume is limited and performance suffers
– The query interface is not modern, interface is a bit behind
– Redshift needs more flexibility to create user-defined functions
– Access to the underlying operating system and certain database functions and capabilities aren’t available
– Starting sizes may be too large for some use cases
– Redshift also resides in a single AWS availability zone

3. Amazon Redshift has several limitation: Limits in Amazon Redshift. On the other hand, you can hardly find a database feature not yet implemented by Oracle.

4. But the most important reason why to migrate to ADWC is that the Oracle Autonomous Database Cloud offers total automation based on machine learning and eliminates human labor, human error, and manual tuning.

How to migrate from Amazon Redshift to Autonomous Data Warehouse Cloud?

Use the SQL Developer Amazon Redshift Migration Assistant which is available with SQL Developer 17.4. It provides easy migration of Amazon Redshift environments on a per-schema basis.

Here are the 5 steps on how to migarte from Amazon Redshift to Autonomous Data Warehouse Cloud:

1. Connect to Amazon Redshift
2. Start the Cloud Migration Wizard
3. Review and Finish the Amazon Redshift Migration
4. Use the Generated Amazon Redshift Migration Scripts
5. Perform the Post Migration Tasks

Check out what Paul Way says about why Oracle thinks Autonomous IT can ultimately win the Cloud War.

Finally, here is what Amazon CTO Werner Vogels is saying: Our cloud offers any database you need. And I agree with him that a one size fits all database doesn’t fit anyone. But mission and business critical enterprise systems with huge requirements and resource needs deserve only the best.

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“To make a vehicle autonomous, you need to gather massive streams of data from loads of sensors and cameras and process that data on the fly so that the car can ‘see’ what’s around it” Daniel Lyons

Let me add that the data must be stored somewhere, analyzed by some software, monitored and backed up by someone, and so on and so on…

Top 5 Industry Early Adopters Of Autonomous Systems are: (1) Information Technology: Oracle’s Autonomous Data Warehouse Cloud, (2) Automotive, (3) Manufacturing, (4) Retail and (5) Healthcare.

Being an early adopter of ADWC, I must say that it is probably the best product created by Oracle Corporation. For sure part of Top Five.

This month (May 2018), ComputerWeekly published an article quoting Oracle CEO Mark Hurd that the long-term future of database administrators could be at risk if every enterprise adopts the Oracle 18c autonomous database.

“Hurd said it could take almost a year to get on-premise databases patched, whereas patching was instant with the autonomous version. If everyone had the autonomous database, that would change to instantaneous.”

So where does that leave Oracle DBAs around the world? Possibly in the unemployment queue, at least according to Hurd.

“There are hundreds of thousands of DBAs managing Oracle databases. If all of that moved to the autonomous database, the number would change to zero,” Hurd said at an Oracle media event in Redwood Shores, California.

If you are interested in more detail on this subject, I suggest you read the following articles in the order below:

The Robots are coming by James Anthony: “But surely we’ve been here before? Indeed, a quick Google search brings up the following examples of white papers by Oracle with a reference to the database being self-managing all the way back to 2003.”

Oracle Autonomous Database and the Death of the DBA by Tim Hall: “Myself and many others have been talking about this for over a decade. ”

Death of the DBA, Long Live the DBA by Kellyn Pot’Vin-Gorman: “With DBAs that have been around a while, we know the idea that you don’t need a DBA has been around since Oracle 7, the self-healing database.”

No DBA Required? by Tim Hall: “It will be interesting to see what Oracle actually come up with at the end of all this…”

Self-Driving Databases are Coming: What Next for DBAs? by Maria Colgan: “It’s also important for DBAs to remember that the transition to an autonomous environment is not something that will occur overnight.”

Death of the Oracle DBA (again) by Johanthan Stuart: “Twenty years later I run Claremont’s Managed Services practice and the DBA group is our largest delivery team.”

Don’t Fall For The “Autonomous Database” Distraction by Greg McStravick: a totally different point of view on autonomous databases.

Now, “a picture is worth a thousand words”. Here are 5 screenshots from the Autonomous Data Warehouse Cloud documentation:

1. Who will be creating external tables using the DBMS_CLOUD package?

2. Who will run “alter database property set.. ” in order to create credentials for the Oracle Cloud Infrastructure?

3. Who will restore and recover the database in case of any type of failure? Or failures never happen, right?

4. Who will manage run away SQL with cs_resource_manager and run “alter system kill session”?

5. Who will manage the CBO statistics and add hints?

As of today, we have 4 Exadata choices with Autonomous being by far the best. For data warehouse loads for now. As explained by Alan Zeichick, Autonomous Capabilities Will Make Data Warehouses — And DBAs — More Valuable. “No need for a resume writer: DBAs will still have plenty of work to do.”

So still: a database without a DBA is like a tree without roots.

P.S. Check out the book Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty and H. James (Jim) Wilson.

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