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Control-M + Pig
Your Pig workflow starts before a Pig Latin script ever executes — and it can fail when something upstream breaks. An HDFS File Watcher times out before the expected partition arrives. A Sqoop import overruns its scheduled ingestion window. Control-M orchestrates the full chain: HDFS data arrival, Sqoop ingestion, Pig execution, and downstream delivery.
common workflow issues
Does this sound like your week?
HDFS DATA ARRIVAL
Your Pig script is ready. The HDFS partition still isn’t.
Control-M uses HDFS File Watcher criteria to wait for the required file before releasing the Pig job. Processing starts when the expected HDFS input is available instead of at an arbitrary clock time, preventing premature execution against missing data.
SQOOP · PIG
Sqoop import ran late. Pig started before ingestion finished.
Control-M models the Sqoop import and Pig script as dependent jobs, releasing Pig only after the upstream job completes successfully. The processing sequence follows actual execution state rather than separate schedules, preventing Pig from running against incomplete imported data.
HDFS COMMAND FAILURE
Your input arrived. The HDFS preparation command failed before Pig ran.
Control-M can run HDFS commands before the Pig script and, using the FailJobOnCommandFailure setting, determine whether a failed pre-command should fail the job.. Preparation logic and script execution stay within one controlled workflow, stopping downstream processing when the required HDFS operation does not complete.
FAILURE RECOVERY
The Pig job failed overnight. Downstream Hive processing is still waiting.
Control-M detects the failed job state and keeps dependent processing from continuing until recovery conditions are met. Operators can see the failure in its end-to-end workflow context, preventing a failed Pig transformation from cascading into downstream data processing.
SLA RISK
Pig finished successfully. The 6:00 a.m. analytics handoff is still late.
Control-M tracks Pig within the end-to-end service and monitors the dependencies that determine final delivery. SLA monitoring and predictive delay detection expose risk before the downstream handoff misses its target, giving operations teams time to intervene.
Control‑M + Pig
Control‑M + Pig
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workload.types |
Pig Latin scripts · Hadoop batch processing · parameterized Pig jobs · HDFS data transformations |
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trigger.type |
HDFS file arrival (HDFS File Watcher) · time schedule · upstream job completion · Control-M event |
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cross_tool.deps |
HDFS File Watcher · Sqoop job · Hive job · MapReduce job · downstream file delivery |
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cloud.platforms |
Hadoop clusters · on-premises infrastructure · hybrid environments · self-hosted Control-M · Control-M SaaS (Hadoop plug-in provisioned) |
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error_handling |
configurable recovery · upstream failure hold · downstream cascade prevention · pre-command failure handling · post-command failure handling · SLA alerting |
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throughput |
large-scale batch processing · distributed Hadoop execution · parallel data processing · high-volume HDFS workloads |
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observability |
job execution status · job output · workflow monitoring · dependency visibility · SLA tracking · historical execution records |
end-to-end orchestration
One production workflow. Every tool in the stack.
Control-M orchestrates workflows across Pig, HDFS, Sqoop, Hive, file transfers, and cloud services in a single job flow — with dependency tracking, SLA visibility, and automated recovery across all of them.
- Cross-tool dependency: HDFS File Watcher → Sqoop import → Pig script → Hive analytics handoff
- Data-aware triggers: HDFS file arrival, upstream job completion, API event, file delivery
|
Pig |
Pig Latin script execution · parameterized processing · status tracking |
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HDFS |
file arrival monitoring · pre/post commands · data readiness |
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Sqoop |
import execution · dependency coordination · completion tracking |
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Hive |
downstream execution · dependency coordination · analytics handoff |
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MapReduce |
distributed job execution · status tracking · workflow dependencies |
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Managed File Transfer |
secure file movement · arrival monitoring · delivery confirmation |
airflow coexistance
Control‑M doesn’t replace your Airflow DAGs. It runs the layer above them.
Airflow manages its DAG. Control-M manages everything surrounding it.
airflow handles
DAG-level orchestration inside the data pipeline
- DAG-level task orchestration within data pipelines
- Python operators, sensors, and task dependencies
- Execution graph for jobs that run inside your pipeline
- Manages retries within a single DAG context
control-m adds
The coordination layer around your DAGs
- Coordination layer around DAGs — triggers Airflow based on upstream conditions: file arrivals, API events, other tool completions
- Tracks each DAG’s SLA contribution across the full end-to-end workflow, not just its own routine
- Manages failure recovery when upstream dependencies fail before Airflow even starts
- Existing DAGs don’t need to be rewritten or migrated
MONITOR PIPELINES
Monitor Pig jobs and dependencies in one place.
Pig executes transformations inside Hadoop, but production pipelines typically span additional jobs, files, and processing tools. Control-M provides centralized workflow monitoring across those dependencies so data teams can see execution progress and identify where a pipeline is waiting or failing:
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Pig job execution status
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Upstream and downstream dependencies
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Job output and runtime history
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Pipeline failures and waits
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End-to-end workflow visibility
SLA ASSURANCE
Keep Pig-driven data pipelines on schedule.
A successful Pig job does not guarantee that the complete data pipeline will finish on time. Control-M tracks the surrounding workflow and its service-level commitments, helping teams identify delays and intervene before downstream data consumers are affected:
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Predictive SLA delay detection
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End-to-end service visibility
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Dependency-aware execution
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Automated failure handling
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Centralized operational alerts