I am trying to migrate a Datawarehouse to Delta lake. One thing that I am struggling to figure out is how to connect to Delta Lake (silver and gold) tables outside a spark session. I want to able to connect to these tables using BI tools like Tableau. I am not using databricks and I was wondering if storing these tables in the hive metastore could help. If not this then could someone help me with an alternative approach or if this is feasible or not.
You can have a Hive metastore and a Thrift server with Spark open source and delta.io open source then connect Tableau desktop for instance.
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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 have snowflake as a data warehouse and want to use Apache atlas as a data catalog and lineage tool.
I went through the details but not sure it can be used with snowflake.
Is it possible to connect with a snowflake?
Disclaimer: I work for Snowflake.
It is not possible, at this time, to use Atlas as the catalog. You can, however, use a Hive Metastore with Snowflake. I have not tried to use things like the Atlas Hook but perhaps that might be an option.
I'm currently working in a Mainframe Technology where we store the data in IBM DB2.
We got a new requirement to use scalable process to migrate the data to a new messaging platform including new database. For that we have identified Kafka is a suitable solution with either KSQLDB or MONGODB.
Can someone able to tell or direct me on how can we connect to IBM DB2 from Kafka to import the data and place it in either KSQLDB or MONGODB?
Any help is much appreciated.
To import the data from IBM DB2 into Kafka, You need to use any connector like the Debezium connector for DB2.
The information regarding the connector can be found in the following.
https://debezium.io/documentation/reference/connectors/db2.html
Connector Configuration
You can also use JDBC Source Connector for the same functionality. The following links are helpful for the configuration.
https://www.confluent.io/blog/kafka-connect-deep-dive-jdbc-source-connector/
A Simple diagram for events flows from RDMS to Kafka topic.
After placing the data into Kafka, we need to transfer that data MongoDb. We need to use Mongo Db Connector to transfer the data from Kafka to mongo Db.
https://www.mongodb.com/blog/post/getting-started-with-the-mongodb-connector-for-apache-kafka-and-mongodb-atlas
https://www.confluent.io/hub/mongodb/kafka-connect-mongodb
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.
I want to do some analytics using Flink on the Data in Postgresql. How and where should I give the port address,username and password. I was trying with the table source as mentioned in the link:https://ci.apache.org/projects/flink/flink-docs-release-1.4/dev/table/common.html#register-tables-in-the-catalog.
final static ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
final static TableSource csvSource = new CsvTableSource("localhost", port);
I am unable to start with actually. I went through all the documents but detailed report about this not found.
The tables and catalog referred to the link you've shared are part of Flink's SQL support, wherein you can use SQL to express computations (queries) to be performed on data ingested into Flink. This is not about connecting Flink to a database, but rather it's about having Flink behave somewhat like a database.
To the best of my knowledge, there is no Postgres source connector for Flink. There is a JDBC table sink, but it only supports append mode (via INSERTs).
The CSVTableSource is for reading data from CSV files, which can then be processed by Flink.
If you want to operate on your data in batches, one approach you could take would be to export the data from Postgres to CSV, and then use a CSVTableSource to load it into Flink. On the other hand, if you wish to establish a streaming connection, you could connect Postgres to Kafka and then use one of Flink's Kafka connectors.
Reading a Postgres instance directly isn't supported as far as I know. However, you can get realtime streaming of Postgres changes by using a Kafka server and a Debezium instance that replicates from Postgres to Kafka.
Debezium connects using the native Postgres replication mechanism on the DB side and emits all record inserts, updates or deletes as a message on the Kafka side. You can then use the Kafka topic(s) as your input in Flink.