hi everyone, starting out with meltano and trying to configure snowflake as my target destination. Currently have this setup in my

meltano.yml

file but keep receiving a "loader failed" error. Using key-pair auth with private key in

.env

file - any thoughts? Tried debugging but logs are unclear to me. Thanks!

loaders:
  - name: target-snowflake
    variant: meltanolabs
    pip_url: meltanolabs-target-snowflake
    config:
      account: xxxx
      add_record_metadata: false
      database: production
      default_target_schema: public
      role: xxxx
      schema: xxxx
      user: xxxx
      warehouse: default

Hi team - I'm a data analyst at Qogita and I'm looking to potentially set up your self host infrastructure - before I do I just want to check that you offer tapfiliate as 1 of your taps as this is the main reason I would want to set it up - hope to hear from you soon 😄

Hi melters Is anyone able to guide me on the easiest way in 2025 to get SQLFluff working for my meltano project? I want that auto-formatting so so badly.

Hi All, I have a more general question. When working with taps, is it normal for the catalog I generate to have issues for my targets? What I mean is that I am using tap-googleads with a target-postgres. If I generate the catalog and run a test with a target-jsonl, all works fine. However, if I use the stock catalog and try this with postgres, I need to go in and make lots of changes. Some examples of changes are column naming notation and selection of primary keys. I expected a catalog generation to generate something that all loaders could use but I'm starting to see that is not the case. I do not mind making these updates but want to make sure it it the proper work flow. The end result seems to be a pretty customized catalog file that I need to reference from meltano.yml.

Hi there, I'm trying to test out

tap-sharepointsites

and I am getting this error

2025-03-04T11:39:22.288098Z [error    ] Extractor 'tap-sharepointsites' could not be installed: Failed to install plugin 'tap-sharepointsites'.
2025-03-04T11:39:22.288141Z [info     ] ERROR: Ignored the following versions that require a different python version: 0.0.1 Requires-Python >=3.7.1,<3.11
ERROR: Could not find a version that satisfies the requirement tap-sharepointsites (from versions: none)
ERROR: No matching distribution found for tap-sharepointsites

Need help fixing this problem? Visit <http://melta.no/> for troubleshooting steps, or to
join our friendly Slack community.

Failed to install plugin(s)

Originally, I had Python 3.11 so I used pyenv to downgrade to Python 3.10 but it still seems like it doesn't want to find the right version. Does anyone have any ideas about what I should try out?

When I run

meltano invoke dbt-postgres:run

can I specify just one source to transform?

Hi 👋 looking into Meltano for our ingestion from GCP Cloud SQL into BigQuery. Running:

meltano run tap-postgres target-bigquery

Is there a way to load the data into bigquery columns directly, instead of one json "data" column?

I haven't used Meltano for a while but I'm trying to get back to it now - I have a CI pipeline I built a long time ago but I've noticed it's giving me this error when trying to install Meltano:

No matching distribution found for meltano==2.10.0

Was this version yanked? I can still see it in PyPI

Hi all, new meltano user here. I am running into a "BrokenPipeError", and im not sure why. I have a csv file in my desktop and I trying to store in a parquet file. Here is also my meltano.yml. I thought it could be file size but right now my file has 10k rows. Thank you!

version: 1
default_environment: dev
project_id: 2fc4aa94-ed4d-49cd-9b6b-c1644bf4608e
environments:
- name: dev
- name: staging
- name: prod
plugins:
  extractors:
  - name: tap-spreadsheets-anywhere
    variant: ets
    pip_url: git+<https://github.com/ets/tap-spreadsheets-anywhere.git>
    config:
      tables:
      - path: 'file:///Users/juanherrera/Desktop/subway-monthly-data'
        name: 'subway_monthly_data'
        pattern: 'MTA_Subway_Hourly_Ridership_small.csv'
        start_date: '2025-03-12T15:30:00Z'
        prefer_schema_as_string: true
        key_properties: ['id']
        format: csv

  loaders:
  - name: target-parquet
    variant: automattic
    pip_url: git+<https://github.com/Automattic/target-parquet.git>
    config:
        destination_path: data/subway_data
        compression_method: snappy
        logging_level: info
        disable_collection: true

Hi, I'm new to Meltano and data engg in general, wondering if there's something I'm doing wrong here or if it's just that Hubspot private apps are not yet supported in Meltano (https://github.com/singer-io/tap-hubspot/issues/211). I'm trying to configure the tap-hubspot extractor with my Hubspot private app details. I have the client_id set to my Hubspot private app access token, client_secret set to the client secret, and a redirect_url set as well. When I test the configuration, I keep getting the error "Exception: Config is missing required keys: ['refresh_token']". However, there is no refresh token available that I can see in my Hubspot private app. Is there a way to disable this requirement? I see a section called "overriding discoverable plugin properties" in this documentation (https://docs.meltano.com/guide/configuration) but unsure if that applies here. Thanks for any help you can provide.

What's the suggested approach for handling transformations that are beyond DBT's capabilities? I've got a postgres warehouse, loading CSV data in. I need to do fuzzy matching on company name (e.g. ACME Corp. vs Acme Co. vs Acme). It's brutal in SQL/DBT. Ideally I could write a python script to do it. I see that there are plugins that are 'strongly discouraged'. Is there a recommended approach? Worst case, I'll write a custom script that is outside the meltano pipeline, but was hoping for something that might be more integrated, for example to help deal with incremental updates.

Hi, I’m trying to set up replication from Cloud SQL MySQL to BigQuery and I’m having an issue with tap-mysql not using state. The replication works fine except it always starts from scratch due to not picking up on the state the previous run generated. It does generate the state well when a run completes, but on the next run I always see the following two lines:

2025-03-20T13:59:43.766320Z [debug    ] Could not find state.json in /projects/.meltano/extractors/tap-mysql/state.json, skipping.

2025-03-20T13:59:43.793158Z [warning  ] No state was found, complete import.

and then every table says it requires a full resync. Even when I manually copy the state to the location in the first log line, it doesn’t pick it up. Any ideas?

I am having issues with tap-mssql variant SpaceCondor using log based replication (Change Tracking not CDC): https://hub.meltano.com/extractors/tap-mssql--spacecondor/ I have Change Tracking configured in SQL Server but am getting a full replication on each run when using meltano run (switched to target-jsonl from target-snowflake for debugging):

meltano run tap-mssql target-jsonl

The state gets updated with each run:

{"bookmarks": {"dbo-c_logical_field_user_values": {}}}

Extractor config:

extractors:

- name: tap-mssql

config:

host: PROJECT01

port: 60065

database: rss_test

username: svcTestAccount

default_replication_method: LOG_BASED

sqlalchemy_url_query_options:

- key: driver

value: ODBC Driver 18 for SQL Server

- key: TrustServerCertificate

value: yes

select:

- dbo-c_logical_field_user_values.*

I think this might be a configuration issue, not sure but perhaps isn't picking up the default replication method?

{"event": "Visiting CatalogNode.STREAM at '.streams[352]'.", "level": "debug", "timestamp": "2025-04-22T00:34:06.910101Z"}

{"event": "Setting '.streams[352].selected' to 'False'", "level": "debug", "timestamp": "2025-04-22T00:34:06.910162Z"}

{"event": "Setting '.streams[352].selected' to 'True'", "level": "debug", "timestamp": "2025-04-22T00:34:06.910211Z"}

{"event": "Skipping node at '.streams[352].tap_stream_id'", "level": "debug", "timestamp": "2025-04-22T00:34:06.910259Z"}

{"event": "Skipping node at '.streams[352].table_name'", "level": "debug", "timestamp": "2025-04-22T00:34:06.910306Z"}

{"event": "Skipping node at '.streams[352].replication_method'", "level": "debug", "timestamp": "2025-04-22T00:34:06.910354Z"}

{"event": "Skipping node at '.streams[352].key_properties[0]'", "level": "debug", "timestamp": "2025-04-22T00:34:06.910402Z"}

{"event": "Visiting CatalogNode.PROPERTY at '.streams[352].schema.properties.logical_field_sid'.", "level": "debug", "timestamp": "2025-04-22T00:34:06.910457Z"}

{"event": "Visiting CatalogNode.PROPERTY at '.streams[352].schema.properties.enabled_flag'.", "level": "debug", "timestamp": "2025-04-22T00:34:06.910513Z"}

{"event": "Skipping node at '.streams[352].schema.properties.enabled_flag.maxLength'", "level": "debug", "timestamp": "2025-04-22T00:34:06.910604Z"}

{"event": "Visiting CatalogNode.PROPERTY at '.streams[352].schema.properties.modified_by_user_sid'.", "level": "debug", "timestamp": "2025-04-22T00:34:06.910779Z"}

{"event": "Visiting CatalogNode.PROPERTY at '.streams[352].schema.properties.modified_datetime'.", "level": "debug", "timestamp": "2025-04-22T00:34:06.910924Z"}

{"event": "Skipping node at '.streams[352].schema.properties.modified_datetime.format'", "level": "debug", "timestamp": "2025-04-22T00:34:06.910988Z"}

{"event": "Visiting CatalogNode.PROPERTY at '.streams[352].schema.properties.timestamp'.", "level": "debug", "timestamp": "2025-04-22T00:34:06.911099Z"}

{"event": "Visiting CatalogNode.PROPERTY at '.streams[352].schema.properties.system_modified_datetime'.", "level": "debug", "timestamp": "2025-04-22T00:34:06.911160Z"}

{"event": "Skipping node at '.streams[352].schema.properties.system_modified_datetime.format'", "level": "debug", "timestamp": "2025-04-22T00:34:06.911212Z"}

{"event": "Skipping node at '.streams[352].schema.type'", "level": "debug", "timestamp": "2025-04-22T00:34:06.911262Z"}

{"event": "Skipping node at '.streams[352].schema.required[0]'", "level": "debug", "timestamp": "2025-04-22T00:34:06.911312Z"}

{"event": "Skipping node at '.streams[352].schema.$schema'", "level": "debug", "timestamp": "2025-04-22T00:34:06.911361Z"}

{"event": "Skipping node at '.streams[352].is_view'", "level": "debug", "timestamp": "2025-04-22T00:34:06.911410Z"}

{"event": "Skipping node at '.streams[352].stream'", "level": "debug", "timestamp": "2025-04-22T00:34:06.911458Z"}

{"event": "Visiting CatalogNode.METADATA at '.streams[352].metadata[0]'.", "level": "debug", "timestamp": "2025-04-22T00:34:06.911509Z"}

{"event": "Visiting metadata node for tap_stream_id 'dbo-c_logical_field_user_values', breadcrumb '['properties', 'logical_field_sid']'", "level": "debug", "timestamp": "2025-04-22T00:34:06.911558Z"}

{"event": "Setting '.streams[352].metadata[0].metadata.selected' to 'False'", "level": "debug", "timestamp": "2025-04-22T00:34:06.911616Z"}

{"event": "Setting '.streams[352].metadata[0].metadata.selected' to 'True'", "level": "debug", "timestamp": "2025-04-22T00:34:06.911665Z"}

Anyone have suggestions on troubleshooting? No errors like the previous question about not finding the state. SQL Server tables have Change Tracking enabled in SQL Server as:

ALTER TABLE dbo.' + @ls_table_name + N'

ENABLE CHANGE_TRACKING

WITH (TRACK_COLUMNS_UPDATED = OFF);

Does Meltano support Change Data Capture (CDC) functionality for MySQL?

Hello! I'm starting out in data engineering and I need to integrate a MongoDB database with BigQuery. I found Meltano with a solution for this, but I'm having problems; when I try to test the connection (

meltano config tap-mongodb test

) I get the message:

m-meltano:~/prj-mdb-gbq$ meltano config tap-mongodb test
2025-04-28T17:50:03.990046Z [info     ] The default environment 'dev' will be ignored for `meltano config`. To configure a specific environment, please use the option `--environment=<environment name>`.
2025-04-28T18:03:11.496374Z [warning  ] Stream `classe` was not found in the catalog
Need help fixing this problem? Visit <http://melta.no/> for troubleshooting steps, or to join our friendly Slack community.
Plugin configuration is invalid
No RECORD or BATCH message received. Verify that at least one stream is selected using 'meltano select tap-mongodb --list'.

The meltano.yml looks like this:

version: 1
default_environment: dev
project_id: c1ac854b-545d
environments:
- name: dev
plugins:
  extractors:
  - name: tap-mongodb
    variant: z3z1ma
    pip_url: git+<https://github.com/z3z1ma/tap-mongodb.git>
    config:
      mongo:
        host: 12.34.5.678
        port: 27017
        directConnection: true
        readPreference: primary
        username: datalake
        password: ****
        authSource: db
        tls: false
      strategy: infer
    select:
    - classe.*
    metadata:
      dbprocapi_classe:
        replication_key: replication_key
        replication-method: LOG_BASED

For testing purposes I am trying to load only the "classe" collection (- classe.*) from the db database. When I use the command "`meltano select tap-mongodb --list --all`" I have :

Enabled patterns: classe.*

but also appears in

[excluded   ] db_classe.field1
[excluded   ] db_classe.field2
[excluded   ] db_classe.field3

It is important to note that MongoDB does not have replicas. I'm using: • a VM on Google Cloud to access MongoDB, both on the same network; • the tap-mongodb extractor (z3z1ma). Could someone please help me? Thank you.

We have a bunch of network devices all running the same software. I need to query each of them with the same extractor. The queries will all be the same and the schemas will also all be the same. What's the correct way to do that? I'm planning on using the rest tap but can write my own if that's cleaner. Ideally I would be able to supply a list of hosts to reach out to based on the response from an initial api call to get a list of hosts

The containerization documentation (https://docs.meltano.com/guide/containerization/) recommends

meltano add files files-docker-compose

, but this adds a broken

docker-compose.yml

definition that doesn't start, throwing

Error: No such command 'ui'.

Hello! I am new to Meltano and I’m working on a pipeline using tap-csv to ingest a CSV file and would like to load its data into multiple Postgres tables, depending on column mappings. Has anyone done this with Meltano before? Curious if you’d recommend using stream maps, custom plugins, or something like dbt for post-load splitting. Any best practices or patterns would be greatly appreciated!

Hello! I'm using

target-bigqeury

(z3z1ma) to receive data from MongoDB into BigQuery. I managed to send some collections to the target (not all), but some questions arose. If you could help me when you can, please, I would appreciate it: 1. How can I specify in

target-bigquery

some tables in BigQuery that should be partitioned by field X and clustered by Y, Z? 2. Why are two tables created in BigQuery: one with the execution time suffix, with data, and another without suffix and without data? Is a new table created with each load? (attached file) 3. I would like to confirm: normally there is no change in the MongoDB schema, but it can occur in case of an update. I am using denormalized: true. In case of a change, this can impact the load, correct? 4. The last error I got was "`ParseError: null is not allowed to be used as an element in a repeated field at processo.prioridade[0]`". Is it possible to handle this in stream-maps? Thanks!

Hello, I am trying to setup the rest-api tap. But, there seems to be a problem either with auth or my api url. Is there anyway to see which requests are beeing run by the plugin during config test (config tap-rest-api-msdk test)?

Hi all, I have a question concerning the use_singer_decimal property. My pipeline is from tap-oracle (variant: s7clarke10) to target-snowflake (variant: meltanolabs). The source column MYCOLUMN is Number(20,2) in the Oracle DB. My extractor's properties look like this:

plugins:
  extractors:
  - name: tap-oracle
    variant: s7clarke10
    pip_url: git+<https://github.com/s7clarke10/pipelinewise-tap-oracle.git>
    config:
      ...
      use_singer_decimal: true
    select:
    - TEST-TEST_TABLE.*
    schema:
      TEST-TEST_TABLE:
        MYCOLUMN:
          type: [string, 'null']
          format: x-singer.decimal
          precision: 20
          scale: 2

- The column MYCOLUMN is extracted in the JSON as expected: ...,"MYCOLUMN":"1234.56",... - However, in Snowflake the column MYCOLUMN is created as NUMBER(38,0) instead of NUMBER(20,2) and inserted as the rounded value '1235' - In case I create the target table in advance as NUMBER(20,2), the inserted value looks like '1235.00' What am I missing here / doing wrong?

Hi everyone! I would like to confirm some informations, please: 1. _Does tap-mongodb (

z3z1ma

) support the LOG_BASED replication-method?_ 2. If so, then the MongoDB database needs a replica set, right? Thanks!

Hi Melty Crew, I wanted to set up a toy example of a dbt-meltano-dagster combo. I used this awesome starting guide: https://medium.com/@kenokumura/how-to-orchestrate-dbt-with-dagster-in-multi-containers-on-docker-ebf0d171a3a9 and now added meltano and dagster-meltano to the Dockerfile_user_code, mounted my meltano project, and added a "load_jobs_from_meltano_project" to repo.py. Doing only dagster+meltano or dagster+dbt this way works, but together it breaks (something wrong with snowplow-tracker...), which I suspect is a version thing. To get started, which version of these packages should be compatible here? I trial-errored some combinations but got nowhere. Thanks a lot!!

Hey there, I am using tap-salesforce to pull data, first time full load and then incremental. There are situations when I need to pull data only latest 10 records from Contact or Account. Is it possible to do in MeltanoLab variant?

plugins:
  extractors:
  - name: tap-salesforce
    variant: meltanolabs
    pip_url: git+<https://github.com/MeltanoLabs/tap-salesforce.git@v1.9.0>
    config:
      client_id: xxxxx
      max_workers: 8
      api_type: REST
      instance_url: <https://xx.xx.salesforce.com>
    select:
    - Account.*

Hey, everyone. I’m trying to move some data from MongoDB to Snowflake. We already have a lot of dbt transformations running on Snoflake, so I’m just re-implementing our EL part of the ELT. I’ve decided to use the MongoDB Tap by MeltanoLabs and it successfully connected to my MongoDB database. I’m not sure, though, how am I supposed to: 1. Define which collections (and ideally which of their fields) to get moved to Snowflake (I figured I can use

select

in the

meltano.yml

, but not sure if that’s the right way to do it) 2. Define what strategy to use to sync the data - I’d like to generally use LOG_BASED, but need to do a complete sync first. It sees this particular tap doesn’t support FULL_SYNC. I also couldn’t get

LOG_BASED

to work (I set it up with

metadata

section in the

meltano.yml

file, where I set

*

under which

replication-key: replication_key

and

replication-method: LOG_BASED

) - I get

resume token string was not a valid hex string

which I assume is because I’ve never done a full sync, but when I try to do it, I get an error this tap doesn’t work with full sync. My use case sounds rather standard - I just need to move data from MongoDB (limited set of collections) => Snowflake tracking changes, but without applying any transformations nor parsing. I need initial full import, and log-based import after that, but unfortunately I couldn’t get it to work…

Hi Everyone, Is it possible to store

select:

and

metadata:

in a separate file instead of including them directly in the

meltano.yml

file?

plugins:
  extractors:
  - name: tap-postgres
    variant: meltanolabs
    pip_url: meltanolabs-tap-postgres
    config:
      host: localhost
      port: 5432
      user: postgres
      password: '123123'
      database: postgres
      filter_schemas: [public]
    select:
    - public-work_orders.*
    - public-trips.*
    metadata:
      public-work_orders:
        replication_method: "INCREMENTAL"
        replication_key: created_at
      public-trips:
        replication_method: "INCREMENTAL"
        replication_key: created_at

plugins: extractors: - name: tap-mysql variant: transferwise pip_url: git+https://github.com/transferwise/pipelinewise.git#subdirectory=singer-connectors/tap-mysql config: host: $MYSQL_HOST port: $MYSQL_PORT user: $MYSQL_USER password: $MYSQL_PASSWORD database: $MYSQL_DATABASE use_gtid: true filter_dbs: $MYSQL_DATABASE how can I configure so the tap just get the selected database and selected tables that I want to extract

any of you guys know about this error i'm having when using target-redshift --variant ticketswap? and how to solve it? already checked if my loader is up to date 2025-07-04T121112.175516Z [error ] Loading failed code=1 message="TypeError: prepare_column() missing 1 required positional argument: 'cursor'" name=meltano run_id=f7f213ef-0e5e-4dd2-87c2-45f83e0f983f state_id=2025-07-04T120759--tap-mysql--target-redshift

Is there any documentation on how to config your catalog.json after I run a --discover ? I'm using tap-mysql target-redshift both from transferwise, but I'm having some issues with log_based load method, since every run it runs discover again and it seems to do a full load of the table again, instead of just updating based on the binlog extractors: - name: tap-mysql variant: transferwise pip_url: git+https://github.com/transferwise/pipelinewise.git#subdirectory=singer-connectors/tap-mysql config: host: $MYSQL_HOST port: $MYSQL_PORT user: $MYSQL_USER password: $MYSQL_PASSWORD database: $MYSQL_DATABASE filter_dbs: $MYSQL_DATABASE use_gtid: true select: /*tables ##### */

Hi guys, I have a quick question regarding the best way to handle multiple taps in Meltano, each targeting around 1,000 tables. These tables are filtered from a source database that contains over 5,000 tables. Due to the volume, it's not feasible to list all 1,000 tables under the

select

parameter in the

meltano.yml

file. On the other hand, if I use a custom

catalog.json

, Meltano seems to ignore the tap configuration defined in

meltano.yml

. What would be the recommended approach in this scenario? If there are any documented use cases or best practices for managing large-scale tap configurations like this, I’d really appreciate it if you could share them.