Precisely Connect alternative — Precisely Connect vs Skippr
Precisely Connect is CDC and batch for mainframe and IBM i. Sources include VSAM, IMS, COBOL copybooks, sequential files, Db2 for z/OS, Db2 for i, Db2 for LUW, Oracle, SQL Server, PostgreSQL, MySQL, and Sybase. Targets on the product page include Kafka, Confluent, Hadoop / Hive / Impala, Avro, Parquet, Snowflake, Databricks, Synapse, Teradata, and the usual clouds. Connect CDC talks to Apache Kafka 3.0 and later from version 5.8.10: self-managed brokers, MSK (including Serverless with IAM), Confluent Cloud with Schema Registry, and HDInsight if the brokers speak the standard client. Topologies include one-way, two-way, consolidated, distributed, and cascade. The product used to ship as MIMIX Share and as SQData. Precisely does not publish a price list. Mainframe and IBM i orders pick a capacity metric (MSU, MIPS, LPAR, cores, or site) on the Order. AWS Mainframe Modernization Data Replication with Precisely is the consumption-priced cloud path for IMS, VSAM, Db2 z/OS, and Db2 i into AWS. Iceberg is not on the Connect product page.
Skippr is an open Streamhouse you run as one Rust binary. Write skippr.yml (or the same thing in Python), discover, sync. You query live state over Iceberg ∪ WAL, and the history is ordinary Iceberg in your bucket. No LPAR key. ELT into a warehouse is one path, not the product.
| Feature | Skippr | Precisely Connect |
|---|---|---|
| Deploy | Single Rust binary, your VPC or on-prem | Windows or Linux plus source agents. AWS Mainframe Modernization is the managed path |
| Schema | In-stream evolution, sibling columns | Copybook and relational mapping. Kafka can use Confluent Schema Registry |
| Storage | Open Iceberg on Parquet you own | Kafka, warehouse, Hadoop, or Parquet files. No Iceberg ∪ WAL |
| CDC | Log-based; fails closed if the dest cannot honour | Log CDC from IMS, VSAM, Db2 z/OS, Db2 i, and listed RDBMS |
| Query | skipprd query on Iceberg ∪ WAL; history in any Iceberg engine | Destination after apply. No SQL engine in Connect |
| Pricing | Free self-hosted engine | Quote. Capacity on the Order. AWS path is usage-based. |
What Precisely Connect is good at
IMS and VSAM without a full unload. That is the product. COBOL copybooks into Kafka or Snowflake is why modernization programs still buy it. Two-way and cascade topologies are documented if you need more than a one-way feed. If the job is "mainframe log CDC into MSK," that is a real product.
Where the job is wider
Connect is a replicator. It is not a Streamhouse. There is no Iceberg ∪ WAL. There is no skipprd query on in-flight rows. Kafka or Snowflake is the landing.
License math is the ops. The metric is on the Order, not on a public card. Ironstream, Connect Sort, Syncsort MFX, and Assure MIMIX are different SKUs. Connect CDC licence keys are a support case, not self-serve.
Skippr will not capture IMS, VSAM, or Db2 for i. Skippr also will not be your bidirectional mainframe pair. If the job is "IBM i or z/OS CDC into Kafka," Connect is the narrower tool. If the job is live SQL on Iceberg you run, Connect was never that.
Getting started
Install skipprd from the install guide, or pip install skippr. The three tabs write the same skippr.yml. Then discover, sync, and query.
skippr:
workspace: analytics
skipprd_el_storage_mode: local
pipelines:
orders:
data_source: data_sources.app_db
data_sink: data_sinks.lake
data_sources:
app_db:
Postgres:
host: db.internal
user: skippr_reader
password: ${APP_DB_PASSWORD}
database: app
tables: [orders, customers]
cdc_mode: snapshot_then_cdc
data_sinks:
lake:
AthenaIceberg:
warehouse: s3://my-iceberg-warehouse/
glue_database_name: analytics
athena_workgroup_name: primary
athena_results_s3_bucket: my-athena-results
region: us-east-1
schema_sink: schema_sinks.lake_schema
schema_sinks:
lake_schema:
AthenaIceberg:
warehouse: s3://my-iceberg-warehouse/
glue_database_name: analytics
athena_workgroup_name: primary
athena_results_s3_bucket: my-athena-results
region: us-east-1skipprd --workspace analytics --storage-mode local connect data-source postgres \
--pipeline orders \
--name app_db \
--host db.internal \
--user skippr_reader \
--password '${APP_DB_PASSWORD}' \
--database app \
--tables orders \
--tables customers \
--cdc-mode snapshot_then_cdc
skipprd connect data-sink athenaiceberg \
--pipeline orders \
--name lake \
--warehouse s3://my-iceberg-warehouse/ \
--glue-database-name analytics \
--athena-workgroup-name primary \
--athena-results-s3-bucket my-athena-results \
--region us-east-1
skipprd connect schema-sink athenaiceberg \
--pipeline orders \
--name lake_schemafrom skippr import (
Config,
DataSinkAthenaIceberg,
DataSourcePostgres,
DataSourcePostgresSourceCdcMode,
EnvRef,
LocalStorage,
Pipeline,
)
cfg = Config().workspace("analytics").storage(LocalStorage())
app_db = cfg.data_source(
"app_db",
DataSourcePostgres(
host="db.internal",
user="skippr_reader",
password=EnvRef("APP_DB_PASSWORD"),
database="app",
tables=["orders", "customers"],
cdc_mode=DataSourcePostgresSourceCdcMode.SNAPSHOTTHENCDC,
),
)
lake = cfg.data_sink(
"lake",
DataSinkAthenaIceberg(
warehouse="s3://my-iceberg-warehouse/",
glue_database_name="analytics",
athena_workgroup_name="primary",
athena_results_s3_bucket="my-athena-results",
region="us-east-1",
),
schema_sink="lake_schema",
)
cfg.pipeline("orders", Pipeline(data_source=app_db, data_sink=lake))
cfg.save("skippr.yml")export APP_DB_PASSWORD='your-postgres-password'
skipprd discover --pipeline orders
skipprd sync --pipeline orders --once
skipprd query "SELECT count(*) FROM analytics.orders"Which to choose
Choose Precisely Connect if you need IMS, VSAM, or IBM i log CDC into Kafka or a warehouse, and apply after capture is enough.
Choose Skippr if you want one binary, live SQL, and Iceberg files that stay yours. Skippr will not read a mainframe log.
Install from Install. Connector and query reference live in Developer Docs. For optional onboarding in your VPC, Enterprise Jumpstart or Contact.