How to retrieve column values from redshift and send it over sns? Boto3 has this option to connect to redshift and retrieve data, but can we send this same data immediately to an sns topic? I have a list of batch ids to be sent to email endpoints using sns. Any sample code / other options which can be used are appreciated.
you can use 'psycopg2' module to connect to Redshift. You can normally query the Redshift with the query and get the result set. Redshift is based on the Postgresql.
Then using boto3 as a client, you can publish the result of the query on the sns topic.
You can follow these 2 answers:
How to Load Data into Amazon Redshift via Python Boto3?
AWS: Publish SNS message for Lambda function via boto3 (Python2)
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I'm using Google BigQuery for OLAP, and plan to provision Google Cloud SQL (Postgres) for OLTP.
My plan is to direct stream data from Google BigQuery to Postgres.
I try googling the solution, but the option is only using batch file .
Is it possible for the streaming solution from Google BigQuery to PostgreSQL?
Currently there is no streaming read mechanism for accessing bigquery data as mentioned in this Stackoverflow post.
Hence, you'll have to go for reading data through batch process.
You can also setup manual ETL process to integrate BigQuery to PostgreSQL using Cloud Data Fusion as mentioned in this article.
I need to receive messages to my BigQuery tables and I want to know what is the best way to consume those messages.
My Kafka servers who are at AWS they produce AVRO messages and from what I saw Dataflow needs receive JSON format messages. So I googled and found an article explaining how to receive messages to PubSub, but on PubSub what I only see in this type of architecture, they create a Kafka VM on GCP to produce the messages.
What I need to know is:
It's possible to receive AVRO messages on PubSub from external Kafka Servers and then deserialize the message using my Schema, sending it to Dataflow and finally send it to BigQuery tables?
Or do I need to create a Kafka VM and use it to consume messages from external servers?
This might seem a bit confusing but it is what I am feeling right now. The main goal here is to get messages from Kafka (AVRO format) at AWS and put them on BigQuery tables. If you have any suggestions they are very welcomed
Thanks a lot in advance
The Kafka Connect BigQuery Connector may be exactly what you need. It is a Kafka sink connector that allows you to export messages from Kafka directly to BigQuery. The README page provides detailed configuration instructions, including how to let the connector recognize your Kafka queue and how to enter the information for the destination BigQuery table. This connector should be able to retrieve the AVRO schema automatically from your Kafka project.
I am trying to build a data pipeline from snowflake to Kafka.
Preferably I want to use AWS MSK.
I could find multiple docs for streaming data in to snowflake from Kafka but I am looking for other way around.
I am fetching JSON data from a Kafka topic. I need to dump this data onto GCS (Google Cloud Storage) into a directory, wherein the directory name will be fetched from the value of "ID" in the JSON data.
I googled and did not find any similar use case wherein Kafka Connect can be used to interpret the JSON data and create directories dynamically based on the value from the JSON data.
Can this be achieved using Kafka Connect?
You can use Kafka Connect GCS sink connector which is provided by Confluent.
The Google Cloud Storage (GCS) connector, currently available as a
sink, allows you to export data from Kafka topics to GCS objects in
various formats. In addition, for certain data layouts, GCS connector
exports data by guaranteeing exactly-once delivery semantics to
consumers of the GCS objects it produces.
Here's an example configuration for the connector:
name=gcs-sink
connector.class=io.confluent.connect.gcs.GcsSinkConnector
tasks.max=1
topics=gcs_topic
gcs.bucket.name=#bucket-name
gcs.part.size=5242880
flush.size=3
gcs.credentials.path=#/path/to/credentials/keys.json
storage.class=io.confluent.connect.gcs.storage.GcsStorage
format.class=io.confluent.connect.gcs.format.avro.AvroFormat
partitioner.class=io.confluent.connect.storage.partitioner.DefaultPartitioner
schema.compatibility=BACKWARD
confluent.topic.bootstrap.servers=localhost:9092
confluent.topic.replication.factor=1
# Uncomment and insert license for production use
# confluent.license=
You can find more details for installation and configuration in the link I've provided above.
This isn't really possible out-of-the-box using most connectors. Instead, you can implement your own Kafka Connect sink task that processes Kafka records and then writes them to the correct GCS directories based on your JSON.
Here's the method you'd override in the connector.
Here's a link to the source code for the AWS S3 sink connector.
We are using Kafka connect S3 sink connector that connect to Kafka and load data to S3 buckets.Now I want to load data from S3 buckets to AWS Redshift using Copy command, for that I'm creating my own custom connector.Use case is I want to load data that created over S3 to Redshift in synchronous way, and then next time S3 connector should replace the existing file and again our custom connector load data to S3.
How can I do this using Confluent Kafka Connect,or my other better approach to do same task?
Thanks in advance !
If you want data to Redshift, you should probably just use the JDBC Sink Connector and download the Redshift JDBC Driver into the kafka-connect-jdbc directory.
Otherwise, rather than writing a Connector, you could use Lambda to trigger some type of S3 event notification to do some type of Redshift upload
Alternatively, if you are simply looking to query S3 data, you could use Athena instead without dealing with any databases
But basically, Sink Connectors don't communicate between one another. They are independent tasks that are designed to initially consume from a topic and write to a destination, not necessarily trigger external, downstream systems.
You want to achieve synchronous behaviour from Kafka to redshift then S3 sink connector is not right option.
If you are using S3 sink connector then first put the data into s3 and then externally run copy command to push to S3. ( Copy command is extra overhead )
No customize code or validation can happen before pushing to redshift.
Redshift sink connector has come up with native jdbc library which is equivalent fast to S3 copy command.