Skip to main content

FreeIPA and Hadoop Distributions (HDP / CDH)

FreeIPA is the tool of choice when it comes to implement a security architecture from the scratch today. I don't need to praise the advantages of FreeIPA, it speaks for himself. It's the Swiss knife of user authentication, authorization and compliance.

To implement FreeIPA into Hadoop distributions like Hortonwork's HDP and Cloudera's CDH some tweaks are necessary, but the outcome is it worth. I assume that the FreeIPA server setup is done and the client tools are distributed. If not, the guide from Hortonworks has those steps included, too.

For Hortonworks, nothing more as the link to the documentation is necessary:
https://community.hortonworks.com/articles/59645/ambari-24-kerberos-with-freeipa.html

Ambari 2.4x has FreeIPA (Ambari-6432) support (experimental, but it works as promised) included. The setup and rollout is pretty simple and runs smoothly per Wizard.

For Cloudera it takes a bit more handwork, but it works at the end also perfect and well integrated, but not at the same UI level as Ambari. These steps are necessary to get Cloudera Manager working with FreeIPA:

1. create the CM principal in FreeIPA (example: cdh@ALO.ALT)
2. retrieve the keytab:
 ipa-getkeytab -r -s freeipa.alo.alt -p cdh -k cdh.keytab
3. install ipa-admintools on the Cloudera Manager server 
 yum install ipa-admintools -y
4. place the retrieval-script (from my GitHub) in /opt/cloudera/security/getkeytabs.sh (or another path accessible by cloudera manager), make it executable and owned by cloudera-scm
 chmod 775 /opt/cloudera/security/getkeytabs.sh && chown cloudera-scm: /opt/cloudera/security/getkeytabs.sh
5. Start the Kerberos wizard, but stop after verifying the cdh user
6. Set the configuration [1] for "Custom Kerberos Keytab Retrieval Script" to "/opt/cloudera/security/getkeytabs.sh"
7. resume the Kerberos wizard and follow the steps until its finished and restart the cluster.

Important:

The FreeIPA client from RHEL7 / CentOS 7 uses now memory based keytabs, but Java doesn't support them (yet). To switch back to the file based ticket cache, the config file (/etc/krb5.conf) needs to be altered by commenting default_ccache_name out, which let the client use the default file based ticket cache:


cat /etc/krb5.conf
..
# default_ccache_name = KEYRING:persistent:%{uid}
..


If you need help with distributed systems, backend engineering, or data platforms, check my Services.

Most read articles

Building a Model-Agnostic Multi-Agent System with OpenClaw

Over one week we rebuilt our AI stack around OpenClaw’s multi-agent architecture to avoid provider lock-in and stop wasting premium tokens. By aligning models to tasks, diversifying fallbacks across providers, enforcing minimal tool access, and switching to memory-first workflows with ephemeral sessions, we reduced token usage per task by about 70% and cut our monthly bill by 77% while improving operational resilience. How We Achieved 77% Cost Reduction and Provider Independence Over the past week, we rebuilt our AI infrastructure around OpenClaw’s multi-agent architecture. The result was a 77% cost reduction , provider independence , and a delegation system that routes work to the most cost-effective model for each job. Below is the technical journey of optimizing a 7-agent squad with OpenClaw. The Challenge: Model Provider Lock-In We started with a simple problem: our entire squad defaulted to a single model provider. This created three issues: Cost inefficiency beca...

Why Is Customer Obsession Disappearing?

Many companies trade real customer-obsession for automated, low-empathy support. Through examples from Coinbase, PayPal, GO Telecommunications and AT&T, this article shows how reliance on AI chatbots, outsourced call centers, and KPI-driven workflows erodes trust, NPS and customer retention. It argues that human-centric support—treating support as strategic investment instead of cost—is still a core growth engine in competitive markets. It's wild that even with all the cool tech we've got these days, like AI solving complex equations and doing business across time zones in a flash, so many companies are still struggling with the basics: taking care of their customers. The drama around Coinbase's customer support is a prime example of even tech giants messing up. And it's not just Coinbase — it's a big-picture issue for the whole industry. At some point, the idea of "customer obsession" got replaced with "customer automation," and no...

What are the performance implications of cross-platform execution within Wayang?

Apache Wayang ® enables cross-platform execution across multiple data processing platforms such as Spark, Flink, Java Streams, PostgreSQL or GraphChi. This capability fundamentally changes the performance behavior of distributed data pipelines. Wayang reduces manual data movement by selecting where each operator should run, but crossing platform boundaries still introduces serialization cost, shifts in locality, different memory strategies and new tuning constraints. Understanding these dynamics is essential before adopting Wayang for multi-platform pipelines at scale. Apache Wayang is a cross-platform data processing framework that lets developers run a single logical pipeline across engines such as Apache Spark, Apache Flink or a native Java backend. It provides an abstraction layer and a cost-based optimizer that selects the execution platform for each operator. This flexibility introduces new performance variables that do not exist in single-engine systems. Engine boundaries ...