ZDLRA best practices

 ZDLRA best practices (updated 2025)

I have seen a lot of changes with the ZDLRA over the last 10 years or so, and because of that I wanted to document the best practices to follow that changed up to now, and are also helpful for new existing. (2025).

Because of the changes that have occurred as the product has matured, I wanted to write a blog post on what existing customers should think about doing now, and what new customers should think about.

1. Channel Configuration

There are a few items to talk about with channel configuration. First let's break down the pieces of the channel configuration

a) Library

Version

Up until version 19.26, you could use a shared version of the library (libra.so) based on the OS.  Updates to the library could be downloaded directly from MOS.

Starting with version 19.27 on Linux, you must use the version that is stored in $ORACLE_HOME and patched using OPatch.  More information can be found in the MOS note below.

ZDLRA: Where to download the SBT_LIBRARY (libra.so module) (Doc ID 2219812.1)

Location (channel setting)

In the previous section I mentioned that the library is tied to the version of Oracle starting with 19.26. Prior to this release, the default channel setting for the library with the ZDLRA  was

• "PARMS  'SBT_LIBRARY=$ORACLE_HOME/lib/libra.so"

In order to always utilize the current library for the $ORACLE_HOME, the recommendation is to set the channel setting to

• "SBT_LIBRARY=libra.so" - No path, and it will default to the current $ORACLE_HOME/lib"

    Or

• "SBT_LIBRARY=oracle.zldlra" - This will default to the current $ORACLE_HOME/lib/libra.so

b) Environment

In the environment section, you specify the SEPS wallet location. Because there could be conflicts with other oracle products that use a wallet (like OUD), the default location for the SEPS wallet should be set to the WALLET_ROOT location, rather than within the $ORACLE_HOME location.

In the past,  the channel setting for ZDLRA  was

• "RA_WALLET=location=file:${ORACLE_HOME}/dbs/ra_wallet credential_alias={SEPS entry in wallet}"

The recommendation is to set the WALLET_ROOT in the spfile, and store the SEPS wallet within the server_seps directory under this location. If WALLET_ROOT is set to $ORACLE_BASE/admin/{$DB_NAME}/wallet, the setting would be

• "RA_WALLET=location=file:${ORACLE_BASE}/admin/${DB_NAME}/wallet/server_seps/ credential_alias={SEPS entry in wallet}"

c) TLS or non-TLS

If a customer has the ZDLRA configured to encrypt backups with TLS/SSL, the library, by default, will attempt to encrypt the backup using SSL/HTTPS.  If TLS/SSL is configured as optional, and if the client does not have the certificate information, backups will fail. To avoid this, it is recommended to add the following setting to the channel configuration.  This setting will allow you to send backups even if optional TSL/SSL is configured.

• _RA_NO_SSL=TRUE

d) Space Efficient Encrypted Backups

In order to use Space Efficient Encrypted Backups, you must set the following in your channel configuration. This is only available on linux with DB version 19.18+. Keep in mind this setting will only compress datafile backup pieces.  

• RA_FORMAT=TRUE

Example:

RMAN> CONFIGURE CHANNEL DEVICE TYPE 'SBT_TAPE' PARMS  'SBT_LIBRARY=oracle.zdlra,ENV=(_RA_NO_SSL=TRUE,RA_WALLET=location=file:/u01/app/oracle/admin/orcl/wallet/server_seps credential_alias="tcps://pmdra1-scan1.us.oracle.com:2484/ra1db", RA_FORMAT=true)';

• oracle.zdlra - Use the libra.so from the $ORACLE_HOME for the database
• _RA_NO_SSL=TRUE - Do not send backups encrypted if TLS is optionally enabled. 
• /u01/app/oracle/admin/orcl/wallet/server_seps - Wallet location from WALLET_ROOT
• RA_FORMAT=true - Use space efficient encrypted backups

2. RMAN encryption

One misconception with Space Efficient Encrypted Backups, is that by setting RA_FORMAT=TRUE you are both compressing and encrypting backups. This is not true. You are only compressing the backups. If the tablespace is encrypted with TDE, the backup will remain encrypted, but any non-TDE tablespace backups will be unencrypted.  In order to encrypt the backup pieces (Datafiles, controlfile,. archive logs and spfile) you need to both set an encryption key (if not already set), and set RMAN encryption on.  If you are backing up any tablespaces that are not TDE, you must also ensure you set RA_FORMAT=TRUE.

You should also set the default encryption algorithm to AES256, as most customers use this today rather than the default of AES128

NOTE: Use of RA_FORMAT=TRUE (RMAN Compression) and RMAN encryption is included in the license for ZDLRA and you do not need the ACO or ASO license for backups to the ZDLRA.

3. Real-time redo settings.

a) Default SEPS wallet location

Wallets are becoming more and more common with databases, and this increases the chances that there will be conflict with other Oracle features that also utilize a wallet. To avoid this there is a new default location for the SEPS wallet used by real-time redo. The database will first look in the {WALLET_ROOT}/server_seps location for the wallet.  It is recommended to set the WALLET_ROOT for all databases and use this default location for the SEPS wallet.  Of course you may use links to simplify management.

b) Encrypt Redo

If you have an encryption key set, and you wish to fully encrypt your backups, you also need to ensure that real-time redo is also encrypted. You need to set encryption on the destination for real-time redo

• ENCRYPTION=ON

NOTE: As an additional note, I have a previous blog post on wallets you can find here.  I have worked with a few customers that have run into issues configuring real-time redo because they have other products that are affected by WALLET_OVERRIDE=TRUE.  What I tell customers is that  if they want to continue to use the SEPS wallet for RMAN scripts (i.e. rman target / catalog /@{wallet entry}), then they should create a separate/customer sqlnet.ora file in a different location, and set TNS_ADMIN in their RMAN backup script. 

4. Reserve Space

Reserve space has been difficult to explain, and it has become even more important when implementing a compliance window.  If there is no compliance set, then the reserve space is only interrogated when the ZDLRA runs out of space.  When a compliance window is set, the reserve space is interrogated when backing up to ensure that compliance locked backups fit within the reserved space. If they do not, backups are rejected.  Because of this, I always recommend setting the "auto-tune reserve space" for the policies, especially when setting a compliance window. This setting will automatically adjust the reserve space for you as databases grow.

5. When encrypting backups set "secure mode"

If you need to fully encrypt your backups with RMAN encryption, it is recommended to use "secure mode" on your protection policy. This setting checks all backup pieces (including real-time redo) to ensure that they are RMAN encrypted.  Any unencrypted backup pieces will be rejected.

6. Use racli commands

One of the biggest changes is the increase in the functionality available with racli command vs executing DBMS_RA PL/SQL packages. It is recommended to utilize RACLI commands when possible.  I know when trying to automate onboarding databases, it is much easier to utilize PL/SQL packages.

7. Pair ZDLRAs when using replication

Another change is the ability to pair ZDLRAs that have replication configured. This can be done with the "racli add ra_partner" command.  Using the ZDLRA pairing commands for replication greatly simplifies the configuration of replication. Existing configurations can be converting to using the pairing model.

8. Review types of DB users

There have been a few changes to DB users. There are now 4 types of DB users.

• ADMIN -Used to administer ZDLRA configuration settings
• MONITOR - Read-only account that can view metadata
• REPLICATION - Used to replicate backups between ZDLRAs
• VPC - Used by protected databases to send/received backups
• Insecure=TRUE - VPC user's password will expire, but it can be reset to the same password. This type of user does not support password rollover. Alternately you can set the profile of the VPC user to "NO_EXPIRE". This is less secure, but avoids having the password expire.
• insecure=false - VPC user's password will expire, but this user can leverage password rollover (STIG) to manage password rotation without having backups fail.

NOTE: If you need to rotate password for VPC users you should leverage the STIG/password rollover function that allows you have two passwords associated with the same VPC user as you update wallets.

Summary:

1. Set WALLET_ROOT in the database and store the SEPS in the {WALLET_ROOT}/server_seps directory
2. Ensure the library location in the channel configuration defaults to the current $ORACLE_HOME
3. Set _RA_NO_SSL=TRUE to ensure that converting to TLS/SSL will not cause existing backups to fails
4. Set RA_FORMAT=TRUE to leverage space efficient encrypted backups --> Linux only
5. Enable RMAN encryption, and set algorithm to AES256 --> Linux only
6. Encrypt Real-time redo if applicable
7. If you require fully encrypted backups, set SECURE_MODE on the policy
8. Enable auto-tune of reserved space for all databases, especially those using a compliance window.
9. Use RACLI commands to manage the ZDLRA
10. Pair ZDLRAs when configuring replication
11. Leverage password rollover for VPC users if you require password rotation

Oracle DB 23ai supports external Apache Iceberg tables

 One area that I have been spending a lot of time on is accessing data stored in object storage from within Oracle DB 23ai using external table definitions.

Parquet objects

I started by creating external tables on Parquet objects, and recently I have been testing accessing objects that are cataloged in Apache Iceberg.

Alexey Filanovskiy wrote a great blog post on Oracle Tables and parquet objects you can find here.

Reading through that blog post gives you a good foundation for why parquet formatted objects make sense for external tables.  Oracle DB is able to leverage the metadata in parquet objects to optimize queries.

Defining external tables on parquet objects

Below is an example table definition for an external table accessing parquet objects. In the below example, the parquet objects were stored on ZFSSA using the OCI API.

CREATE TABLE "DW"."CUSTOMER_ADDRESS" 
   (    "CA_ADDRESS_SK" NUMBER(*,0), 
    "CA_ADDRESS_ID" VARCHAR2(100 BYTE) COLLATE "USING_NLS_COMP", 
    "CA_STREET_NUMBER" VARCHAR2(100 BYTE) COLLATE "USING_NLS_COMP", 
    "CA_STREET_NAME" VARCHAR2(100 BYTE) COLLATE "USING_NLS_COMP", 
    "CA_STREET_TYPE" VARCHAR2(100 BYTE) COLLATE "USING_NLS_COMP", 
    "CA_SUITE_NUMBER" VARCHAR2(100 BYTE) COLLATE "USING_NLS_COMP", 
    "CA_CITY" VARCHAR2(100 BYTE) COLLATE "USING_NLS_COMP", 
    "CA_COUNTY" VARCHAR2(100 BYTE) COLLATE "USING_NLS_COMP", 
    "CA_STATE" VARCHAR2(100 BYTE) COLLATE "USING_NLS_COMP", 
    "CA_ZIP" VARCHAR2(100 BYTE) COLLATE "USING_NLS_COMP", 
    "CA_COUNTRY" VARCHAR2(100 BYTE) COLLATE "USING_NLS_COMP", 
    "CA_GMT_OFFSET" NUMBER, 
    "CA_LOCATION_TYPE" VARCHAR2(100 BYTE) COLLATE "USING_NLS_COMP"
   )  DEFAULT COLLATION "USING_NLS_COMP" 
   ORGANIZATION EXTERNAL 
    ( TYPE ORACLE_BIGDATA
      DEFAULT DIRECTORY "DATA_PUMP_DIR"
      ACCESS PARAMETERS
      ( com.oracle.bigdata.credential.name=OCI_ZFS
        com.oracle.bigdata.fileformat=parquet
          com.oracle.bigdata.trimspaces=notrim
       )
      LOCATION
       ( 'oraclebmc://pmdemo-zs71-01-client.us.oracle.com/n/tcptest/b/tcpds_10000g_parquet/o/customer_address/*'
       )
    )
   REJECT LIMIT UNLIMITED 
  PARALLEL ;

What you will notice from this table definition is

• It uses the "ORACLE_BIGDATA" type. This is necessary to create external tables using big data object types, which is currently [csv|textfile|avro|parquet|orc].
• I am specifying a credential. I had already created a credential that contains the login credentials for the OCI API on my ZFSSA.
• The object type for my objects is parquet.
• The location of the object(s) is specified. In my case I am using an "*" as there are multiple objects within this path that make up the table.
• The format of the URL is in this case "ORACLEBMC". The options are
• ORACLEBMC - Specifies this is an OCI API object storage.
• S3 - Specifies amazon S3 object storage
• S3h- Specifies a non-standard S3 object store

You can find additional information here.

Notes:

1. The format of the URL is not well documented, I also found that in some cases "s3a" can be used for non-AWS s3 object storage.
2. DBMS_CLOUD does not allow you to use URLs other than "https". You will most likely have to create tables using the above syntax rather than using the DBMS_CLOUD package.

Apache Iceberg

Once I was able to successfully access parquet objects directly, I began testing cataloging parquet objects with Apache Iceberg using Spark.

I found the most difficult part of this was properly creating an Apache Iceberg manifest file that could be used to build an external table definition against Iceberg.

Environment to build manifest

My testing environment to build the Apache Iceberg manifest contains the following.

Python

• Python 3.9.18
• Below is a list of my python packages that are installed

Python 3.9.18
Package             Version
------------------- -----------
aiobotocore         2.23.0
aiohappyeyeballs    2.6.1
aiohttp             3.12.14
aioitertools        0.12.0
aiosignal           1.4.0
annotated-types     0.7.0
async-timeout       5.0.1
attrs               20.3.0
Babel               2.9.1
bcc                 0.28.0
boto3               1.39.7
botocore            1.38.27
cachetools          5.5.2
certifi             2020.12.5
cffi                1.14.5
chardet             4.0.0
circuitbreaker      1.3.2
click               8.1.8
cloud-init          23.4
cockpit             311.1
colorama            0.4.6
configobj           5.0.6
configshell-fb      1.1.30
cryptography        36.0.1
dasbus              1.4
dbus-python         1.2.18
distro              1.5.0
docutils            0.16
file-magic          0.4.0
frozenlist          1.7.0
fsspec              2025.7.0
gpg                 1.15.1
greenlet            3.2.3
halo                0.0.31
idna                2.10
Jinja2              2.11.3
jmespath            1.0.1
jsonpatch           1.21
jsonpointer         2.0
jsonschema          3.2.0
kmod                0.1
Ksplice Uptrack     1.2.84
libcomps            0.1.18
lockfile            0.12.2
log-symbols         0.0.14
markdown-it-py      3.0.0
MarkupSafe          1.1.1
mdurl               0.1.2
mmh3                5.1.0
multidict           6.6.3
netaddr             0.8.0
netifaces           0.10.6
nftables            0.1
numpy               2.0.2
nvmetcli            0.7
oauthlib            3.1.1
oci                 2.126.3
oci-utils           0.14.0
oracledb            3.2.0
pandas              2.3.1
parquet_tools       0.2.16
pciutils            2.3.6
pcp                 5.0
perf                0.1
pexpect             4.8.0
pip                 25.1.1
ply                 3.11
prettytable         0.7.2
propcache           0.3.2
psutil              5.8.0
ptyprocess          0.6.0
py4j                0.10.9.9
pyarrow             20.0.0
pycparser           2.20
pycurl              7.43.0.6
pydantic            2.11.7
pydantic_core       2.33.2
Pygments            2.19.2
PyGObject           3.40.1
pyiceberg           0.9.1
pyOpenSSL           19.0.0
pyparsing           3.2.3
pyrsistent          0.17.3
pyserial            3.4
PySocks             1.7.1
pyspark             4.0.0
python-daemon       2.2.4
python-dateutil     2.9.0.post0
python-dmidecode    3.12.2
python-linux-procfs 0.7.3
pytz                2021.1
pyudev              0.22.0
PyYAML              5.4.1
requests            2.25.1
rhnlib              2.8.6
rich                13.9.4
rpm                 4.16.1.3
s3fs                2025.7.0
s3transfer          0.13.0
sdnotify            0.3.2
selinux             3.6
sepolicy            3.6
setools             4.4.4
setroubleshoot      3.3.32
setuptools          53.0.0
six                 1.15.0
sortedcontainers    2.4.0
sos                 4.7.0
spinners            0.0.24
SQLAlchemy          2.0.41
strictyaml          1.7.3
systemd-python      234
tabulate            0.9.0
tenacity            9.1.2
termcolor           3.1.0
thrift              0.16.0
typing_extensions   4.14.0
typing-inspection   0.4.1
tzdata              2025.2
urllib3             1.26.5
urwid               2.1.2
wrapt               1.17.2
yarl                1.20.1

PySpark & Spark runtime

PySpark version: 4.0.0
Java version: 21.0.7
Scala version: version 2.13.16

Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 4.0.0
      /_/

Using Scala version 2.13.16, OpenJDK 64-Bit Server VM, 21.0.7
Branch HEAD
Compiled by user wenchen on 2025-05-19T07:58:03Z
Revision fa33ea000a0bda9e5a3fa1af98e8e85b8cc5e4d4
Url https://github.com/apache/spark

 Environment variables

HADOOP_OPTS= -Djava.library.path=/usr/local/hadoop/hadoop-3.4.1/lib/ -Djava.library.path=/usr/local/hadoop/hadoop-3.4.1/lib/ -Djava.library.path=/usr/local/hadoop/hadoop-3.4.1/lib/
JAVA_HOME=/usr/lib/jvm/java-21-openjdk-21.0.7.0.6-1.0.1.el9.x86_64
HADOOP_HOME=/usr/local/hadoop/hadoop-3.4.1
SPARK_HOME=/usr/local/spark
HADOOP_COMMON_LIB_NATIVE_DIR=/usr/local/hadoop/hadoop-3.4.1/lib/native/

Script to build manifest and store python files

I used the NY State Taxi data to build my objects, and it can be found here.

For my testing, I used the yellow taxi datasets.

Below is the section of my code that builds the spark catalog on OCI S3 object storage, and sets the environment using my credentials.

NOTE: It points to my endpoint and uses my access key and secret. I removed those from the script.

Python script to set the environment and hadoop catalog (Part #1)

from pyspark.sql import SparkSession

ICEBERG = "org.apache.iceberg:iceberg-spark-runtime-4.0_2.13:1.10.0"
HADOOP_CLOUD = "org.apache.spark:spark-hadoop-cloud_2.13:4.0.0"

spark = (
    SparkSession.builder
      .appName("iceberg-oci-s3")
      .config("spark.jars.packages", ",".join([ICEBERG, HADOOP_CLOUD]))
      # Iceberg catalog -> Hadoop catalog in an OCI S3 warehouse
      .config("spark.sql.catalog.oci", "org.apache.iceberg.spark.SparkCatalog")
      .config("spark.sql.catalog.oci.type", "hadoop")
      .config("spark.sql.catalog.oci.warehouse", "s3a://icebergs3/iceberg_warehouse")
      # OCI S3-compatible endpoint
      .config("spark.hadoop.fs.s3a.endpoint", "https://{my tenancy/namespace}.compat.objectstorage.us-ashburn-1.oraclecloud.com")
      .config("spark.hadoop.fs.s3a.path.style.access", "true")
      .config("spark.hadoop.fs.s3a.endpoint.region", "us-ashburn-1")
      .config("spark.hadoop.fs.s3a.aws.credentials.provider", "org.apache.hadoop.fs.s3a.SimpleAWSCredentialsProvider")
      .config("spark.hadoop.fs.s3a.list.version", "1")
      .getOrCreate()
)


hconf = spark.sparkContext._jsc.hadoopConfiguration()
hconf.set("fs.s3a.access.key",    "{my access key}")            # OCI 'Customer Secret Key' ID
hconf.set("fs.s3a.secret.key",    "{my secret for that key}")   # the secret key
hconf.set("fs.s3a.connection.ssl.enabled", "true")

Python script to create namespace and create table (Part #2)

spark.sql("""
  CREATE NAMESPACE IF NOT EXISTS oci_db
""")

import pyarrow.parquet as pq


spark.sql("""
  CREATE NAMESPACE IF NOT EXISTS oci.tripdata
""")

spark.sql("""
  CREATE TABLE IF NOT EXISTS oci.tripdata.yellow (
  `VendorID` INT,
  `tpep_pickup_datetime` TIMESTAMP,
  `tpep_dropoff_datetime` TIMESTAMP,
  `passenger_count` BIGINT,
  `trip_distance` DOUBLE,
  `RatecodeID` BIGINT,
  `store_and_fwd_flag` STRING,
  `PULocationID` INT,
  `DOLocationID` INT,
  `payment_type` BIGINT,
  `fare_amount` DOUBLE,
  `extra` DOUBLE,
  `mta_tax` DOUBLE,
  `tip_amount` DOUBLE,
  `tolls_amount` DOUBLE,
  `improvement_surcharge` DOUBLE,
  `total_amount` DOUBLE,
  `congestion_surcharge` DOUBLE,
  `Airport_fee` DOUBLE
  )
  USING iceberg
  PARTITIONED BY  (months(tpep_pickup_datetime))
""")

Python scrypt to read parquet object/set type/append to iceberg table  (Part #3)

sdf = spark.read.parquet("{parquet object}")  # Location and name of parquet object to load

from pyspark.sql import functions as F
sdf_cast = (
    sdf
    .withColumn("VendorID", F.col("VendorID").cast("int"))
    .withColumn("tpep_pickup_datetime", F.col("tpep_pickup_datetime").cast("timestamp"))
    .withColumn("tpep_dropoff_datetime", F.col("tpep_dropoff_datetime").cast("timestamp"))
    .withColumn("passenger_count", F.col("passenger_count").cast("bigint"))
    .withColumn("trip_distance", F.col("trip_distance").cast("double"))
    .withColumn("RatecodeID", F.col("RatecodeID").cast("bigint"))
    .withColumn("store_and_fwd_flag", F.col("store_and_fwd_flag").cast("string"))
    .withColumn("PULocationID", F.col("PULocationID").cast("int"))
    .withColumn("DOLocationID", F.col("DOLocationID").cast("int"))
    .withColumn("payment_type", F.col("payment_type").cast("bigint"))
    .withColumn("fare_amount", F.col("fare_amount").cast("double"))
    .withColumn("extra", F.col("extra").cast("double"))
    .withColumn("mta_tax", F.col("mta_tax").cast("double"))
    .withColumn("tip_amount", F.col("tip_amount").cast("double"))
    .withColumn("tolls_amount", F.col("tolls_amount").cast("double"))
    .withColumn("improvement_surcharge", F.col("improvement_surcharge").cast("double"))
    .withColumn("total_amount", F.col("total_amount").cast("double"))
    .withColumn("congestion_surcharge", F.col("congestion_surcharge").cast("double"))
    .withColumn("Airport_fee", F.col("Airport_fee").cast("double"))
)

sdf_cast.writeTo("oci.tripdata.yellow").append()

Summary :

Combining the 3 parts of the script, filling in the credentials along with the endpoint, and bucket, along with specifying the parquet object will load the object storage properly including the manifest file.

Investigating the resulting manifest and objects

Below is a list of the objects created in the metadata directory.

You can see that I updated the data and added more data, and each time, it created a new version of the manifest along with snapshot information.

When writing the data, I chose to partition it, below is the partitioned data. You can see that when Apache Iceberg wrote the data, it automatically created parquet objects in directories for each partition, and added to the current directory with new data.

Creating an Oracle table on top of Iceberg

Now that I have created my iceberg table, and it is stored in object storage, I can create an Oracle table that will read the manifest file.

In my example, the most recent manifest object is "iceberg_warehouse/tripdata/yellow/metadata/v7.metadata.json".

My External Table definition is below.

CREATE TABLE CUSTOMERS_ICEBERG
(
VendorID        integer,
tpep_pickup_datetime    timestamp,
tpep_dropoff_datetime    timestamp,
passenger_count        number,
trip_distance        number,
RatecodeID        number,
store_and_fwd_flag    blob,
PULocationID        number,
DOLocationID        number,
payment_type        number,
fare_amount        number,
extra            number,
mta_tax            number,
tip_amount        number,
tolls_amount        number,
improvement_surcharge    number,
total_amount        number,
congestion_surcharge    number,
Airport_fee        number
)
ORGANIZATION EXTERNAL
(
    TYPE ORACLE_BIGDATA
    DEFAULT DIRECTORY DATA_PUMP_DIR
    ACCESS PARAMETERS
    (
        com.oracle.bigdata.fileformat=parquet
        com.oracle.bigdata.credential.name=OCI_S3
        com.oracle.bigdata.access_protocol=iceberg
    )
   LOCATION ('iceberg:https://{namespace}.compat.objectstorage.us-ashburn-1.oci.customer-oci.com/icebergs3/iceberg_warehouse/tripdata/yellow/metadata/v7.metadata.json')
)
PARALLEL;

NOTE: my location is "iceberg:https".

I can now select from my new table and it will read the Iceberg manifest file.

Conclusion:

Oracle Database 23ai not only supports creating external tables on top of Parquet objects, but it also supports creating external tables on top of Apache Iceberg manifest objects.

Building a Cyber Vault ? Don't forget your keys

When building a cyber vault, one of the most important items to manage is encryption keys.  Encrypting your data is a fundamental pillar of ransomware protection, but encryption key management is often forgotten.  

Ensuring your Cyber Vault has a current copy of your encryption keys associated with your Oracle Databases is critically important for a successful restore and recover after an attack.

Starting with the Oracle Key Vault (OKV) 21.11 release, Oracle Key Vault includes a preview of the Key Transfer Across Oracle Key Vault Clusters Integration Accelerator. You can use this feature to transfer security objects from one OKV cluster to another.

You can find more detail on this feature here, and I will describe it's benefits in this post.

The diagram below shows a typical cyber vault architecture to protect Oracle Databases with the Zero Data Loss Recovery Appliance (ZDLRA) and OKV .

Transferring keys into a cyber vault with OKV

Encryption Key Architecture

Encryption key management is a critical piece of data protection, and it is important to properly manage your keys.  Good cyber protection begins with proper encryption key management.

Local Wallets

With Oracle databases, the default (and simplest) location for encryption keys is in a locally management wallet file.  The keys are often stored in an auto-login wallet, which is automatically opened by the database at startup making key management transparent and simple, but not very secure.

Why aren't wallets secure ?

• They are often auto-login (cwallet.sso) which allows the database to open the wallet without requiring a password. This wallet file gives full access to the encryption keys.
• The wallet file is stored with the database files.  A privileged administrator, such as a DBA has access to both the database and the keys to decrypt the data directly from the hosts.  They also have the ability to delete the wallet file.
• Often the wallet file is backed up with the database, which includes an auto-login wallet.  This allows anyone who has access to backups, to also be able to decrypt the data.
• Securely backing up the wallet file separate from the database is often forgotten, especially when ASM is used as the wallet location.  Not having the wallet file when restoring the database makes restoration and recovery impossible.

Steps you can take

• Create both a passworded wallet (ewallet.p12) and local auto-login wallet (cwallet.sso). With a local auto-login wallet, the wallet can only be opened on the host where the wallet was created.
• Backup the passworded wallet only (ewallet.p12).  The auto-login wallet can always be recreated from the passworded wallet.
• Properly store the password for you passworded encryption wallet. You will need the password to rotate your encryption keys, and create the auto-login wallet.

Oracle Key Vault (OKV)

 The best way to securely manage encryption keys is with OKV.

Why ?

• Keys are managed outside of the database and cannot be accessed locally outside of the database.
• Access to keys is granted to a specific database instance.
• OKV is clustered for High Availability, and the OKV cluster can be securely backed up.
• Key access is audited to allow for early detection of access.

Encryption Keys in a cyber vault architecture

Below is a diagram of a typical cyber vault architecture using ZDLRA.  Because the backups are encrypted, either because the databases are using TDE and/or they are creating RMAN encrypted backups sent to the ZDLRA, the keys need to be available in the vault also.

Not only is the cyber vault a separate, independent database and backup architecture, the vault also contains a separate, independent OKV cluster.

This isolates the cyber vault from any attack to the primary datacenter, including any attack that could compromise encryption key availability.

Encryption Keys in an advanced cyber vault architecture

Below is a diagram of an advanced cyber vault architecture using ZDLRA.  Not only are the backups replicated to a separate ZDLRA in the vault, they are internally replicated to an Isolated Recovery Environment (IRE).  In this architecture, the recover area is further isolated, and the OKV cluster is even further isolated from the primary datacenter. This provides the highest level of protection.

OKV Encryption Key Transfer

This blog post highlights the benefit of the newly released (21.11) OKV feature to allow for the secure transfer of encryption keys.

Periodic rotation of encryption keys is a required practice to protect encryption keys, and ensuring you have the current key available in a cyber vault is challenging.

OKV solves this challenge by providing the ability to transfer any new or changed keys between clusters.

Implementing OKV in a cyber Vault

When building a cyber vault, it is recommend to build an independent OKV cluster.  The OKV cluster in the vault is isolated from the primary datacenter and protected by an airgap.  The nodes in this cluster are  not be able to communicate with the OKV cluster outside of the vault.

The OKV cluster in the vault can be created using a full, secure, backup from the OKV cluster in the primary datacenter. The backup can be transferred into the vault, and then restored to the new, independent OKV cluster providing a current copy of the encryption keys.

Keeping OKV managed keys updated in a cyber Vault

The challenge, once creating an isolated OKV cluster has been keeping the encryption keys within the cluster current when new keys are created.  This was typically accomplished by transferring a full backup of OKV into the vault, and rebuilding the cluster using this backup.

OKV 21.11 provides the solution with secure Encryption Key Transfer.  Leveraging this feature you can securely transfer just the keys that have recently changed allowing you to manage independent OKV clusters that are synchronized on a regular basis.

The diagram below shows the flow of the secure Encryption Key Transfer package from the primary OKV cluster into the vault when the air-gap is opened.

This new OKV feature provides a much better way to securely manage encryption keys in a Cyber Vault.

Summary

As ransomware attacks increase, it is critical to protect a backup copy of your critical database in a cyber vault.  It is also critical to protect a copy of your encryption keys to ensure you can recovery those databases.  OKV provides the architecture for key management in both your primary datacenter and in a cyber vault.  The new secure key transfer feature within OKV allows you to synchronize keys across independent OKV clusters.