Kafka Connect is used to build data pipelines by integrating Kafka with other data systems. It uses plugins called connectors and transformations. Transformations allow modifying data going from Kafka to Elasticsearch. Single message transformations apply to individual messages while Kafka Streams is better for more complex transformations involving multiple messages. When using Kafka Connect to sink data to Elasticsearch, best practices include managing indices by day, removing unnecessary fields, and not overwriting the _id field. Custom transformations can be implemented if needed. The ordering of transformations matters as they are chained.
KSQL is an open source, Apache 2.0 licensed streaming SQL engine that enables stream processing against Apache Kafka. KSQL makes it easy to read, write, and process streaming data in real-time, at scale, using SQL-like semantics.
Fundamentals and Architecture of Apache KafkaAngelo Cesaro
Fundamentals and Architecture of Apache Kafka.
This presentation explains Apache Kafka's architecture and internal design giving an overview of Kafka internal functions, including:
Brokers, Replication, Partitions, Producers, Consumers, Commit log, comparison over traditional message queues.
KSQL is an open source, Apache 2.0 licensed streaming SQL engine that enables stream processing against Apache Kafka. KSQL makes it easy to read, write, and process streaming data in real-time, at scale, using SQL-like semantics.
Fundamentals and Architecture of Apache KafkaAngelo Cesaro
Fundamentals and Architecture of Apache Kafka.
This presentation explains Apache Kafka's architecture and internal design giving an overview of Kafka internal functions, including:
Brokers, Replication, Partitions, Producers, Consumers, Commit log, comparison over traditional message queues.
Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
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Introduction and Overview of Apache Kafka, TriHUG July 23, 2013mumrah
Apache Kafka is a new breed of messaging system built for the "big data" world. Coming out of LinkedIn (and donated to Apache), it is a distributed pub/sub system built in Scala. It has been an Apache TLP now for several months with the first Apache release imminent. Built for speed, scalability, and robustness, Kafka should definitely be one of the data tools you consider when designing distributed data-oriented applications.
The talk will cover a general overview of the project and technology, with some use cases, and a demo.
Learn All Aspects Of Apache Kafka step by step, Enhance your skills & Launch Your Career, On-Demand Course
for apache kafka online training visit: https://mindmajix.com/apache-kafka-training
Apache Kafka® is the technology behind event streaming which is fast becoming the central nervous system of flexible, scalable, modern data architectures. Customers want to connect their databases, data warehouses, applications, microservices and more, to power the event streaming platform. To connect to Apache Kafka, you need a connector!
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This is Part 2 of 2 in Building Kafka Connectors - The Why and How
Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
In this Kafka Tutorial, we will discuss Kafka Architecture. In this Kafka Architecture article, we will see API’s in Kafka. Moreover, we will learn about Kafka Broker, Kafka Consumer, Zookeeper, and Kafka Producer. Also, we will see some fundamental concepts of Kafka.
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013mumrah
Apache Kafka is a new breed of messaging system built for the "big data" world. Coming out of LinkedIn (and donated to Apache), it is a distributed pub/sub system built in Scala. It has been an Apache TLP now for several months with the first Apache release imminent. Built for speed, scalability, and robustness, Kafka should definitely be one of the data tools you consider when designing distributed data-oriented applications.
The talk will cover a general overview of the project and technology, with some use cases, and a demo.
Learn All Aspects Of Apache Kafka step by step, Enhance your skills & Launch Your Career, On-Demand Course
for apache kafka online training visit: https://mindmajix.com/apache-kafka-training
Apache Kafka® is the technology behind event streaming which is fast becoming the central nervous system of flexible, scalable, modern data architectures. Customers want to connect their databases, data warehouses, applications, microservices and more, to power the event streaming platform. To connect to Apache Kafka, you need a connector!
This online talk dives into the new Verified Integrations Program and the integration requirements, the Connect API and sources and sinks that use Kafka Connect. We cover the verification steps and provide code samples created by popular application and database companies. We will discuss the resources available to support you through the connector development process.
This is Part 2 of 2 in Building Kafka Connectors - The Why and How
(Randall Hauch, Confluent) Kafka Summit SF 2018
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Bravo Six, Going Realtime. Transitioning Activision Data Pipeline to StreamingYaroslav Tkachenko
Activision Data team has been running a data pipeline for a variety of Activision games for many years. Historically we used a mix of micro-batch microservices coupled with classic Big Data tools like Hadoop and Hive for ETL. As a result, it could take up to 4-6 hours for data to be available to the end customers.
In the last few years, the adoption of data in the organization skyrocketed. We needed to de-legacy our data pipeline and provide near-realtime access to data in order to improve reporting, gather insights faster, power web and mobile applications. I want to tell a story about heavily leveraging Kafka Streams and Kafka Connect to reduce the end latency to minutes, at the same time making the pipeline easier and cheaper to run. We were able to successfully validate the new data pipeline by launching two massive games just 4 weeks apart.
Bravo Six, Going Realtime. Transitioning Activision Data Pipeline to Streamin...HostedbyConfluent
Activision Data team has been running a data pipeline for a variety of Activision games for many years. Historically we used a mix of micro-batch microservices coupled with classic Big Data tools like Hadoop and Hive for ETL. As a result, it could take up to 4-6 hours for data to be available to the end customers.
In the last few years, the adoption of data in the organization skyrocketed. We needed to de-legacy our data pipeline and provide near-realtime access to data in order to improve reporting, gather insights faster, power web and mobile applications. I want to tell a story about heavily leveraging Kafka Streams and Kafka Connect to reduce the end latency to minutes, at the same time making the pipeline easier and cheaper to run. We were able to successfully validate the new data pipeline by launching two massive games just 4 weeks apart.
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Fast and Simplified Streaming, Ad-Hoc and Batch Analytics with FiloDB and Spa...Helena Edelson
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Machbase Neo is an innovative iot data processing solution that integrates various features into an #all_in_one timeseries database.
In the past, development organizations had to invest a lot of time and resources to build a single service or solution. Moreover, they had to navigate complex and challenging processes for data collection and processing. But now, with the introduction of Machbase Neo, these problems have been solved. You can now set up everything using just one Machbase Neo server, allowing developers to focus on their core tasks. This product can save developers over 90% of their time by eliminating unnecessary tasks.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
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Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
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Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
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Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
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5. Data Format Standard is important!
Enter Section/Running Header Here
syntax="proto2";
message envelope {
# required fields
required string data_type = 1;
required string create_at_us = 1;
required string source_name = 1;
# optional
string schema = 1;
# payload
bytes payload = 1;
}
6. • To query Kafka message in real-time
• To quickly find the location of a message
• To trace for a historic event for debugging/diagnose
• To monitor data quality in the pipeline
• To monitor and project data volume in the pipeline for capacity planning
• To detect abnormal data patterns
Why?
Enter Section/Running Header Here
7. • Quick overview of Kafka Connect
• How does the data transformation work in Kafka Connect
• What is SMT
• Some use cases for SMT
• SMT vs Kafka Streams for data transformation
• Tips for using Kafka Connect to sink data to Elasticsearch
Takeaways
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9. • Lightweight and stateless
• Scalable and fault-tolerant
• Integrates with Kafka and many other data systems
• Pluggable architecture make customization easy and configurable
• Lots open source (connectors and converter plugins) available
• Run in two modes:
• standalone mode is great for dev and local testing
• distributed mode is great for scaling and fault-tolerance
• REST API available to monitor and configure your connectors in the distributed
mode
More reasons to use Kafka Connect
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11. ● Default AVRO or JSON or write your own
● Configurable
○ Different data converter for key and value
○ Specify how null, invalid or malformed message should be handled
● Kafka Connect isolates each plugin from one another so that libraries in one plugin
are not affected by the libraries in any other plugins
○ `plugin.path` is configured in the Kafka Connect worker configuration
○ Build your JAR with dependency and copy it to `plugin.path`.
Plugin: Data Converter
Enter Section/Running Header Here
# directory other than the home directory of Confluent Platform.
plugin.path=share/java
12. # Data converter plugin
value.converter.protoClassName=net.demonware.pipes.connect.data.proto.MessageEnvelopeOuterClass$Mess
ageEnvelope
Plugin: Data Converter
Enter Section/Running Header Here
14. • Modifies messages going out of Kafka before it reaches Elasticsearch
• One message at a time
• Many built-in SMT are already available
• Flexible within the constraints of the TransformableRecord API and 1:{0,1}
mapping
• Transformation is chained
• Pluggable transformers through Connect configuration
What is SMT?
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15. Default Kafka Connect SMT
Enter Section/Running Header Here
Field Name Included in Kibana
InsertField Insert field using attributes from the record metadata or a configured
static value.
MaskField Mask specified fields with a valid null value for the field type.
ReplaceField Filter or rename fields.
TimestampConverter Convert timestamps between different formats such as Unix epoch,
strings, and Connect Date and Timestamp types.
TimestampRouter Update the record’s topic field as a function of the original topic value
and the record timestamp.
RegexRouter Update the record topic using the configured regular expression and
replacement string.
16. Field Name Included in Kibana
Cast Cast fields or the entire key or value to a specific type, e.g. to force an
integer field to a smaller width.
ExtractField Extract the specified field from a Struct when schema present, or a Map
in the case of schemaless data. Any null values are passed through
unmodified
ExtractTopic Replace the record topic with a new topic derived from its key or value.
Flatten Flatten a nested data structure. This generates names for each field by
concatenating the field names at each level with a configurable delimiter
character.
HoistField Wrap data using the specified field name in a Struct when schema
present, or a Map in the case of schemaless data.
ValueToKey Replace the record key with a new key formed from a subset of fields in
the record value.
17. • An alias in transforms implies that some additional keys are configurable.
• Syntax:
• transforms.$alias.type – fully qualified class name for the transformation
• transforms.$alias.* – all other keys as defined in Transformation.config() are
embedded with this prefix
• Example:
Configuring SMT
Enter Section/Running Header Here
transforms.insertKafkaMetadata.type=org.apache.kafka.connect.transforms.InsertField$Value
transforms.insertKafkaMetadata.topic.field=kafka_topic
transforms.removeFields.type=org.apache.kafka.connect.transforms.ReplaceField$Value
transforms.removeFields.blacklist=context,tracing,payload
transforms.convertTimestampUnit.type=net.demonware.pipes.kafka.connect.transforms.ConvertTimeToMillis$Value
transforms.convertTimestampUnit.timestamp.fields=created_at_us,ingested_at_us
18. • SMT is chained
• SMT are applied in the order they are specified in `transforms`.
• If your transformation is order dependent then need to make sure they are specified in the correct order
• Example:
Ordering of SMT matters!
Enter Section/Running Header Here
transforms=insertKafkaMetadata,indexMapping
transforms.indexMapping.type:org.apache.kafka.connect.transforms.TimestampRouter
transforms.indexMapping.topic.format:topic-changed-${timestamp}
transforms.indexMapping.timestamp.format:yyyy.MM.dd
transforms.insertKafkaMetadata.type=org.apache.kafka.connect.transforms.InsertField$Value
transforms.insertKafkaMetadata.topic.field=kafka_topic
19. • Only if you cannot use the built-in and cannot use Kafka Streams for the data
transformation.
• Must implement the Transformation interface.
• Consider to make your SMT configurable.
• If you have multiple custom SMT, better to have separate Transformation
implementation.
Create Custom SMT
Enter Section/Running Header Here
20. // Existing base class for SourceRecord and SinkRecord, new self type parameter.
public abstract class ConnectRecord<R extends ConnectRecord<R>> {
// ...
// New abstract method:
/** Generate a new record of the same type as itself, with the specified parameter values. **/
public abstract R newRecord(String topic, Schema keySchema, Object key, Schema valueSchema, Object value, Long timestamp);
}
public interface Transformation<R extends ConnectRecord<R>> extends Configurable, Closeable {
// via Configurable base interface:
// void configure(Map<String, ?> configs);
/**
* Apply transformation to the {@code record} and return another record object (which may be {@code record} itself) or {@code null},
* corresponding to a map or filter operation respectively. The implementation must be thread-safe.
*/
R apply(R record);
/** Configuration specification for this transformation. **/
ConfigDef config();
/** Signal that this transformation instance will no longer will be used. **/
@Override
void close();
}
Interface: Transformation
25. ● Recommended practice in general
● Transformation involves multiple
messages, such as aggregation.
● More complex transformation:
aggregation, windowing, joining
● When the transformed data will be
consumed by multiple downstream
consumers. Reduce overhead by
running transformation only once and
allow reuse.
● Lightweight and simple data
transformation.
● Covered by the Kafka Connect
built-in SMT
● Data footprint cost is a concern.
Large amount of transformed data
written back to Kafka is too costly.
● Simplicity in streaming data pipeline
is important. Want to keep data
pipeline stage and services to a
minimum
● Transformation does not interact with
external systems
SMT
Enter Section/Running Header Here
Kafka Streams
27. • Overwrite the ES @timestamp internal field
• Overwrite the document ‘_id’ field to have control over how should your data be
de-duplicated
• Remove unnecessary columns/fields to save space and footprint of your ES cluster.
• Manage your ES Index by day. You can use ‘TimestampRouter’ and ‘RegexRouter’ SMT to
generate ES indice per day for your data.
• Have binary data available for search in a user-friendly format then you need to transform
the binary data prior to indexing.
“123e4567-e89b-12d3-a456-426655440000”
Do’s
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28. ● Some cosmetic data format tweaking can be done in Kibana
○ Date display format
○ Base64 Decode binary data for display
○ Type casting from integer to text
● If you need to modify Kafka Connect source code for any reason then you might want to
reconsider using Kafka Connect
○ it can be hard to debug and test. Maybe you should consider Kafka Streams instead
● When implement your own transformation, keep each transformation implementation
separate rather than have a single transformation class that does a bunch of things.
Don’ts
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