I'm new to Kafka/AWS.My requirement to load data's from several sources into DW(Redshift).
One of my sources is PostgreSQL. I found a good article using Kafka to Sync data into Redshift.
This article is more good enough to sync the data between the PostgreSQL to redshift.But my requirement is to transform the data's before loading into Redshift.
Can somebody help me to how to transform the data's in Kafka (PostgreSQL->Redhsift)?
Thanks in Advance
Jay
Here's an article I just published on exactly this pattern, describing how to use Apache Kafka's Connect API, and KSQL (which is built on Kafka's Streams API) to do streaming ETL: https://www.confluent.io/ksql-in-action-real-time-streaming-etl-from-oracle-transactional-data
You should check out Debezium for streaming events from Postgres into Kafka.
For this, you can use any streaming application be it storm/spark/kafka streaming. These application will consume data from diff sources and the data transformation can be done on the fly. All three have their own advantages and complexity.
Related
I'm trying to solve the problem of data denormalization before indexing to the Elasticsearch. Right now, my Postgres 11 database is configured with pgoutput plugin and Debezium with Postgresql Connector is streaming the log changes to RabbitMq which are then aggregated by doing a reverse lookup on the db and feeding to the Elasticsearch.
Although, this works okay, the lookup at the App layer to aggregate the data is expensive and taking a lot of execution time (the query is already refined but it has about 10 joins making it sloppy).
The other alternative I explored was to use KStreams for data aggregation. My knowledge on Apache Kafka is minimal and thus I'm here. My question here is it a requirement to have Apache Kafka as the broker to be able to utilize the Java KStreams API or can it be leveraged with any broker such as RabbitMq? I'm unsure about this because all the articles talk about Kafka Topics and Key Value pairs which are specific to Apache Kafka.
If there is a better way to solve the data denormalization problem, I'm open to it too.
Thanks
Kafka Steams is only for Kafka. You're more than welcome to use Kafka Streams between Debezium and the process that consumes any topic (the Postgres connector that writes to RabbitMQ?)
You can use Spark, Flink, or Beam for stream processing on other messaging queues, but Debezium requires Kafka so start with tools around that.
Spark, for example, has an Elasticsearch writer library; not sure about the others.
I have a requirement to capture changes from a stream of data. Below given is my solution.
Data Flows into Kafka -> Consumer Picks up data and inserts/updates (trimmed data) to DynamoDB(We have configured DynamoDB Streams). After every insert/update a stream is generated with changed data, which is then interpreted and processed by a Lambda.
Now my question is if have to replace DynamoDB with Couchbase, will Couchbase provide a CDC out of the box? I am pretty much new to Couchbase and I tried searching for the CDC feature but no direct documentation.
Any pointers would be very helpful! Thanks!
Couchbase has an officially supported Kafka Connector (documentation here).
I'm not familiar with the "CDC" term, but this Couchbase Kafka connector can act as both a sink and a source.. It's not "out of the box" per se, it's a separate connector.
It seems Change Data Capture (CDC) isn't supported in Couchbase but there are features to notify when documents change. For example, the Source Kafka Connector use that and send documents when changing, including metadata when configured with DefaultSchemaSourceHandler, which should be close enough to CDC:
https://docs.couchbase.com/kafka-connector/current/quickstart.html#defaultschemasourcehandler
My current project is in MainFrames with DB2 as its database. We have 70 databases with nearly 60 tables in each of them. Our architect proposed a plan of using Kafka with Spark streaming for processing data. How good is Kafka in reading the RDBMS tables for data ? Do we directly read the data from the tables using Kafka or is there any other way to get the data from RDBMS into Kafka ?
If there is any better solution, your suggestions can help a lot.
Do not directly read from database, it will create additional load. I would suggest two approaches.
Send new data both to databases and to Kafka, or send it to Kafka and then consume for processing.
Read data from database write ahead log (I know it is possible for MySQL with Maxwell but I am not sure for DB2) and send it to Kafka for further processing.
You can use Spark Streaming or Kafka Streams depending on your needs.
I want to setup Flink so it would transform and redirect the data streams from Apache Kafka to MongoDB. For testing purposes I'm building on top of flink-streaming-connectors.kafka example (https://github.com/apache/flink).
Kafka streams are being properly red by Flink, I can map them etc., but the problem occurs when I want to save each recieved and transformed message to MongoDB. The only example I've found about MongoDB integration is flink-mongodb-test from github. Unfortunately it uses static data source (database), not the Data Stream.
I believe there should be some DataStream.addSink implementation for MongoDB, but apparently there's not.
What would be the best way to achieve it? Do I need to write the custom sink function or maybe I'm missing something? Maybe it should be done in different way?
I'm not tied to any solution, so any suggestion would be appreciated.
Below there's an example what exactly i'm getting as an input and what I need to store as an output.
Apache Kafka Broker <-------------- "AAABBBCCCDDD" (String)
Apache Kafka Broker --------------> Flink: DataStream<String>
Flink: DataStream.map({
return ("AAABBBCCCDDD").convertTo("A: AAA; B: BBB; C: CCC; D: DDD")
})
.rebalance()
.addSink(MongoDBSinkFunction); // store the row in MongoDB collection
As you can see in this example I'm using Flink mostly for Kafka's message stream buffering and some basic parsing.
As an alternative to Robert Metzger answer, you can write your results again to Kafka and then use one of the maintained kafka's connectors to drop the content of a topic inside your MongoDB Database.
Kafka -> Flink -> Kafka -> Mongo/Anything
With this approach you can mantain the "at-least-once semantics" behaivour.
There is currently no Streaming MongoDB sink available in Flink.
However, there are two ways for writing data into MongoDB:
Use the DataStream.write() call of Flink. It allows you to use any OutputFormat (from the Batch API) with streaming. Using the HadoopOutputFormatWrapper of Flink, you can use the offical MongoDB Hadoop connector
Implement the Sink yourself. Implementing sinks is quite easy with the Streaming API, and I'm sure MongoDB has a good Java Client library.
Both approaches do not provide any sophisticated processing guarantees. However, when you're using Flink with Kafka (and checkpointing enabled) you'll have at-least-once semantics: In an error case, the data is streamed again to the MongoDB sink.
If you're doing idempotent updates, redoing these updates shouldn't cause any inconsistencies.
If you really need exactly-once semantics for MongoDB, you should probably file a JIRA in Flink and discuss with the community how to implement this.
In Hadoop world, flume or kafka is used to streaming or collecting data and store them in Hadoop. I am just wondering that does Mango DB has some similar mechanisms or tools to achieve the some?
MongoDB is just the database layer, not the complete solution like the Hadoop ecosystem. I actually use Kafka along with Storm to store data in MongoDB in cases where there is a very large flow of incoming data which needs to be processed and stored.
Although Flume is frequently used and treated as a member of the Hadoop ecosystem, it's not impossible to use it with other sources/sinks. MongoDB is not an exception. In fact, Flume is flexible enough to be extended to create your own custom sources/sinks. See this project, for example. This is a custom Flume-Mongo-sink.