#apache kafka

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I try to make a balance between reading the book "Kafka: The Definitive Guide - 2nd Edition," doing Confluent course lab exercises, and a little bit of Udemy projects with Kafka as well. In the middle of the week, I'm making my homepage to showcase some portfolio stuff, which is not my priority at this time, but it involves a lot of coding as well.

Feeling like I can answer any interview questions about Kafka at this point, including the fundamentals, use cases, and examples of writing a pub/sub system in Java.

It's all about studying; it magically changes you inside and out. You're the same person, in the same place, but now capable of creating really good software with refined techniques.

[govindhtech]

Bigtable SQL Introduces Native Support for Real-Time Queries

Upgrades to Bigtable SQL offer scalable, fast data processing for contemporary analytics. Simplify procedures and accelerate business decision-making.

Businesses have battled for decades to use data for real-time operations. Bigtable, Google Cloud's revolutionary NoSQL database, powers global, low-latency apps. It was built to solve real-time application issues and is now a crucial part of Google's infrastructure, along with YouTube and Ads.

Continuous materialised views, an enhancement of Bigtable's SQL capabilities, were announced at Google Cloud Next this week. Maintaining Bigtable's flexible schema in real-time applications requires well-known SQL syntax and specialised skills. Fully managed, real-time application backends are possible with Bigtable SQL and continuous materialised views.

Bigtable has gotten simpler and more powerful, whether you're creating streaming apps, real-time aggregations, or global AI research on a data stream.

The Bigtable SQL interface is now generally available.

SQL capabilities, now generally available in Bigtable, has transformed the developer experience. With SQL support, Bigtable helps development teams work faster.

Bigtable SQL enhances accessibility and application development by speeding data analysis and debugging. This allows KNN similarity search for improved product search and distributed counting for real-time dashboards and metric retrieval. Bigtable SQL's promise to expand developers' access to Bigtable's capabilities excites many clients, from AI startups to financial institutions.

Imagine AI developing and understanding your whole codebase. AI development platform Augment Code gives context for each feature. Scalability and robustness allow Bigtable to handle large code repositories. This user-friendliness allowed it to design security mechanisms that protect clients' valuable intellectual property. Bigtable SQL will help onboard new developers as the company grows. These engineers can immediately use Bigtable's SQL interface to access structured, semi-structured, and unstructured data.

Equifax uses Bigtable to store financial journals efficiently in its data fabric. The data pipeline team found Bigtable's SQL interface handy for direct access to corporate data assets and easier for SQL-savvy teams to use. Since more team members can use Bigtable, it expects higher productivity and integration.

Bigtable SQL also facilitates the transition between distributed key-value systems and SQL-based query languages like HBase with Apache Phoenix and Cassandra.

Pega develops real-time decisioning apps with minimal query latency to provide clients with real-time data to help their business. As it seeks database alternatives, Bigtable's new SQL interface seems promising.

Bigtable is also previewing structured row keys, GROUP BYs, aggregations, and a UNPACK transform for timestamped data in its SQL language this week.

Continuously materialising views in preview

Bigtable SQL works with Bigtable's new continuous materialised views (preview) to eliminate data staleness and maintenance complexity. This allows real-time data aggregation and analysis in social networking, advertising, e-commerce, video streaming, and industrial monitoring.

Bigtable views update gradually without impacting user queries and are fully controllable. Bigtable materialised views accept a full SQL language with functions and aggregations.

Bigtable's Materialised Views have enabled low-latency use cases for Google Cloud's Customer Data Platform customers. It eliminates ETL complexity and delay in time series use cases by setting SQL-based aggregations/transformations upon intake. Google Cloud uses data transformations during import to give AI applications well prepared data with reduced latency.

Ecosystem integration

Real-time analytics often require low-latency data from several sources. Bigtable's SQL interface and ecosystem compatibility are expanding, making end-to-end solutions using SQL and basic connections easier.

Open-source Apache Large Table Washbasin Kafka

Companies utilise Google Cloud Managed Service for Apache Kafka to build pipelines for Bigtable and other analytics platforms. The Bigtable team released a new Apache Kafka Bigtable Sink to help clients build high-performance data pipelines. This sends Kafka data to Bigtable in milliseconds.

Open-source Apache Flink Connector for Bigtable

Apache Flink allows real-time data modification via stream processing. The new Apache Flink to Bigtable Connector lets you design a pipeline that modifies streaming data and publishes it to Bigtable using the more granular Datastream APIs and the high-level Apache Flink Table API.

BigQuery Continuous Queries are commonly available

BigQuery continuous queries run SQL statements continuously and export output data to Bigtable. This widely available capability can let you create a real-time analytics database using Bigtable and BigQuery.

Python developers may create fully-managed jobs that synchronise offline BigQuery datasets with online Bigtable datasets using BigQuery's Python frameworks' bigrames streaming API.

Cassandra-compatible Bigtable CQL Client Bigtable is previewed.

Apache Cassandra uses CQL. Bigtable CQL Client enables developers utilise CQL on enterprise-grade, high-performance Bigtable without code modifications as they migrate programs. Bigtable supports Cassandra's data migration tools, which reduce downtime and operational costs, and ecosystem utilities like the CQL shell.

Use migrating tools and Bigtable CQL Client here.

Using SQL power via NoSQL. This blog addressed a key feature that lets developers use SQL with Bigtable. Bigtable Studio lets you use SQL from any Bigtable cluster and create materialised views on Flink and Kafka data streams.

[jvalentino2]

An example of how to run Kafka with Zookeeper via docker compose.

[ryanvgates]

AWS MSK Create & List Topics

Problem I needed to created topics in Amazon Web Services(AWS) Managed Streaming for Apache Kafka(MSK) and I wanted to list out the topics after they were created to verify. Solution This solution is written in python using the confluent-kafka package. It connects to the Kafka cluster and adds the new topics. Then it prints out all of the topics for verification This file contains…

[associative07]

Mastering Real-Time Data Flows: Associative’s Apache Kafka Expertise

The modern business landscape demands real-time data processing capabilities. From monitoring live sensor data to analyzing customer interactions, the ability to handle continuous streams of information has become essential. This is where Apache Kafka, a distributed streaming platform, and Associative’s Kafka development prowess come into play.

Understanding Apache Kafka

Apache Kafka is an open-source platform designed for building real-time data pipelines and streaming applications. Its core features include:

High-Throughput: Kafka can handle millions of messages per second, making it ideal for large-scale data-intensive applications.

Fault-Tolerance: Kafka’s distributed architecture provides resilience against node failures, ensuring your data remains available.

Scalability: Kafka seamlessly scales horizontally, allowing you to add more nodes as data volumes grow.

Publish-Subscribe Model: Kafka uses a pub-sub messaging pattern, enabling flexible communication between data producers and consumers.

The Associative Advantage in Kafka Development

Associative’s team of Apache Kafka specialists helps you harness the platform’s power to drive your business forward:

Real-Time Data Pipelines: We design and build scalable Kafka-powered pipelines for seamless real-time data ingestion, processing, and distribution.

Microservices Integration: We use Kafka to decouple microservices, ensuring reliable communication and fault tolerance in distributed applications.

IoT and Sensor Data: We build Kafka-centric solutions to manage the massive influx of data from IoT devices and sensors, enabling real-time insights.

Event-Driven Architectures: We help you leverage Kafka for event-driven architectures that promote responsiveness and agility across your systems.

Legacy System Modernization: We integrate Kafka to bring real-time capabilities to your legacy systems, bridging the gap between old and new.

Benefits of Partnering with Associative

Tailored Kafka Solutions: We tailor our solutions to your exact business requirements for a perfect fit.

Pune-Based Collaboration: Experience seamless interaction with our team, thanks to our shared time zone.

Focus on Results: We emphasize delivering measurable business outcomes through our Kafka solutions.

Proven Kafka Success

Associative’s portfolio of successful Kafka projects speaks for itself. Our expertise helps you:

Improve Operational Efficiency: Kafka-powered solutions can streamline processes, reducing costs and improving performance.

Enhance Customer Experiences: React to customer behavior in real-time, personalizing offerings and boosting satisfaction.

Enable Data-Driven Decision Making: Extract real-time insights from streaming data to inform strategic decisions.

Ready to Embrace Real-Time Data with Kafka?

Contact Associative today to learn how our Apache Kafka development services can transform how you handle and leverage real-time data. Let’s build a robust, scalable, and responsive data infrastructure for your business.

[nitor-infotech]

What is Apache Kafka?

Apache Kafka is designed to handle real-time data feeds that provide a high-throughput, resilient, and scalable solution for processing and storing streams of records. The platform ensures durability by replicating data across multiple brokers in a cluster.

Kafka’s exceptional speed is coordinated by two key virtuosos:

Sequential I/O: Kafka addresses the perceived slowness of disks by brilliantly implementing Sequential I/O.

Zero Copy Principle: With this principle, Kafka avoids unnecessary data copies and reduces context switches between user and kernel modes, making it more efficient. 

Why Kafka?

High performance: It has the capability to handle millions of messages per second

Non-volatile storage: It stores messages on disk, which enables durability and fault-tolerance

 Distributed architecture: It can handle large amounts of data and scale horizontally by adding more machines to the cluster.

Learn more about Apache Kafka read our full blog - https://bit.ly/3urUEWF

[smarttechie]

Apache Kafka vs Apache Pulsar - Which one to choose?

In today’s data-driven world, the ability to process and analyze real-time data streams is crucial for businesses. Two open-source platforms, Apache Kafka and Apache Pulsar, have emerged as leaders in this space. But which one is right for you? Market Share and Community: Apache Kafka: Commands a dominant 70% market share, boasting a vast user base and extensive ecosystem of tools and…

[sandipanks]

Spark Streaming and Apache Kafka are real-time data processing systems with distinct applications. Spark Streaming is suitable for real-time data processing, ETL transformations, and machine learning, while Kafka is ideal for high-throughput, distributed data input. Organizations should evaluate their needs and use cases before choosing.

Apache Kafka vs Apache Spark Streaming: Understanding the Key Differences

[softqubetechnologies]

Apache Kafka: A Comprehensive Guide to Real-time Data Streaming and Processing

Explore our complete guide on Apache Kafka, an open-source distributed event streaming platform. Learn about its architecture, installation, use cases, and how to build scalable real-time data processing applications. https://www.softqubes.com/blog/apache-kafka-a-comprehensive-guide-to-real-time-data-streaming-and-processing/

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Apache Kafka, the open-source, distributed event streaming platform, has been a game-changer in the world of data processing and integration.

[varunsngh007]

Does Apache Kafka handle schema?

Apache Kafka does not natively handle schema enforcement or validation, but it provides a flexible and extensible architecture that allows users to implement schema management if needed. Kafka itself is a distributed streaming platform designed to handle large-scale event streaming and data integration, providing high throughput, fault tolerance, and scalability. While Kafka is primarily concerned with the storage and movement of data, it does not impose any strict schema requirements on the messages it processes. As a result, Kafka is often referred to as a "schema-agnostic" or "schema-less" system.

However, the lack of schema enforcement may lead to challenges when processing data from diverse sources or integrating with downstream systems that expect well-defined schemas. To address this, users often implement external schema management solutions or rely on schema serialization formats like Apache Avro, JSON Schema, or Protocol Buffers when producing and consuming data to impose a degree of structure on the data. Apart from it  by obtaining Apache Kafka Certification, you can advance your career as a Apache Kafka. With this course, you can demonstrate your expertise in the basics of afka architecture, configuring Kafka cluster, working with Kafka APIs, performance tuning and, many more fundamental concepts.

By using these serialization formats and associated schema registries, producers can embed schema information into the messages they produce, allowing consumers to interpret the data correctly based on the schema information provided. Schema registries can store and manage the evolution of schemas, ensuring backward and forward compatibility when data formats change over time.

Moreover, some Kafka ecosystem tools and platforms, like Confluent Schema Registry, provide built-in support for schema management, making it easier to handle schema evolution, validation, and compatibility checks in a distributed and standardized manner. This enables developers to design robust, extensible, and interoperable data pipelines using Kafka, while also ensuring that data consistency and compatibility are maintained across the ecosystem. Overall, while Apache Kafka does not handle schema enforcement by default, it provides the flexibility and extensibility needed to incorporate schema management solutions that align with specific use cases and requirements.

[jcmarchi]

Basil Faruqui, BMC Software: How to nail your data and AI strategy - AI News

New Post has been published on https://thedigitalinsider.com/basil-faruqui-bmc-software-how-to-nail-your-data-and-ai-strategy-ai-news/

Basil Faruqui, BMC Software: How to nail your data and AI strategy - AI News

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BMC Software’s director of solutions marketing, Basil Faruqui, discusses the importance of DataOps, data orchestration, and the role of AI in optimising complex workflow automation for business success.

What have been the latest developments at BMC?

It’s exciting times at BMC and particularly our Control-M product line, as we are continuing to help some of the largest companies around the world in automating and orchestrating business outcomes that are dependent on complex workflows. A big focus of our strategy has been on DataOps specifically on orchestration within the DataOps practice. During the last twelve months we have delivered over seventy integrations to serverless and PaaS offerings across AWS, Azure and GCP enabling our customers to rapidly bring modern cloud services into their Control-M orchestration patterns. Plus, we are prototyping GenAI based use cases to accelerate workflow development and run-time optimisation.

What are the latest trends you’ve noticed developing in DataOps?

What we are seeing in the Data world in general is continued investment in data and analytics software. Analysts estimate that the spend on Data and Analytics software last year was in the $100 billion plus range. If we look at the Machine Learning, Artificial Intelligence & Data Landscape that Matt Turck at Firstmark publishes every year, its more crowded than ever before. It has 2,011 logos and over five hundred were added since 2023. Given this rapid growth of tools and investment, DataOps is now taking center stage as companies are realising that to successfully operationalise data initiatives, they can no longer just add more engineers. DataOps practices are now becoming the blueprint for scaling these initiatives in production. The recent boom of GenAI is going make this operational model even more important.

What should companies be mindful of when trying to create a data strategy?

As I mentioned earlier that the investment in data initiatives from business executives, CEOs, CMOs, CFOs etc. continues to be strong. This investment is not just for creating incremental efficiencies but for game changing, transformational business outcomes as well. This means that three things become very important. First is clear alignment of the data strategy with the business goals, making sure the technology teams are working on what matters the most to the business. Second, is data quality and accessibility, the quality of the data is critical. Poor data quality will lead to inaccurate insights. Equally important is ensuring data accessibility – making the right data available to the right people at the right time. Democratising data access, while maintaining appropriate controls, empowers teams across the organisation to make data-driven decisions. Third is achieving scale in production. The strategy must ensure that Ops readiness is baked into the data engineering practices so its not something that gets considered after piloting only.

How important is data orchestration as part of a company’s overall strategy?

Data Orchestration is arguably the most important pillar of DataOps. Most organisations have data spread across multiple systems – cloud, on-premises, legacy databases, and third-party applications. The ability to integrate and orchestrate these disparate data sources into a unified system is critical. Proper data orchestration ensures seamless data flow between systems, minimising duplication, latency, and bottlenecks, while supporting timely decision-making.

What do your customers tell you are their biggest difficulties when it comes to data orchestration?

Organisations continue to face the challenge of delivering data products fast and then scaling quickly in production. GenAI is a good example of this. CEOs and boards around the world are asking for quick results as they sense that this could majorly disrupt those who cannot harness its power. GenAI is mainstreaming practices such as prompt engineering, prompt chaining etc. The challenge is how do we take LLMs and vector databases, bots etc and fit them into the larger data pipeline which traverses a very hybrid architecture from multiple-clouds to on-prem including mainframes for many. This just reiterates the need for a strategic approach to orchestration which would allow folding new technologies and practices for scalable automation of data pipelines. One customer described Control-M as a power strip of orchestration where they can plug in new technologies and patterns as they emerge without having to rewire every time they swap older technologies for newer ones.

What are your top tips for ensuring optimum data orchestration?

There can be a number of top tips but I will focus on one, interoperability between application and data workflows which I believe is critical for achieving scale and speed in production.  Orchestrating data pipelines is important, but it is vital to keep in mind that these pipelines are part of a larger ecosystem in the enterprise. Let’s consider an ML pipeline is deployed to predict the customers that are likely to switch to a competitor. The data that comes into such a pipeline is a result of workflows that ran in the ERP/CRM and combination of other applications. Successful completion of the application workflows is often a pre-requisite to triggering the data workflows. Once the model identifies customers that are likely to switch, the next step perhaps is to send them a promotional offer which means that  we will need to go back to the application layer in the ERP and CRM. Control-M is uniquely positioned to solve this challenge as our customers use it to orchestrate and manage intricate dependencies between the application and the data layer.

What do you see as being the main opportunities and challenges when deploying AI?

AI and specifically GenAI is rapidly increasing the technologies involved in the data ecosystem. Lots of new models, vector databases and new automation patterns around prompt chaining etc. This challenge is not new to the data world, but the pace of change is picking up. From an orchestration perspective we see tremendous opportunities with our customers because we offer a highly adaptable platform for orchestration where they can fold these tools and patterns into their existing workflows versus going back to drawing board.

Do you have any case studies you could share with us of companies successfully utilising AI?

Domino’s Pizza leverages Control-M for orchestrating its vast and complex data pipelines. With over 20,000 stores globally, Domino’s manages more than 3,000 data pipelines that funnel data from diverse sources such as internal supply chain systems, sales data, and third-party integrations. This data from applications needs to go through complex transformation patterns and models before its available for driving decisions related to food quality, customer satisfaction, and operational efficiency across its franchise network.

Control-M plays a crucial role in orchestrating these data workflows, ensuring seamless integration across a wide range of technologies like MicroStrategy, AMQ, Apache Kafka, Confluent, GreenPlum, Couchbase, Talend, SQL Server, and Power BI, to name a few.

Beyond just connecting complex orchestration patterns together Control-M provides them with end-to-end visibility of pipelines, ensuring that they meet strict service-level agreements (SLAs) while handling increasing data volumes. Control-M is helping them generate critical reports faster, deliver insights to franchisees, and scale the roll out new business services.

What can we expect from BMC in the year ahead?

Our strategy for Control-M at BMC will stay focused on a couple of basic principles:

Continue to allow our customers to use Control-M as a single point of control for orchestration as they onboard modern technologies, particularly on the public cloud. This means we will continue to provide new integrations to all major public cloud providers to ensure they can use Control-M to orchestrate workflows across three major cloud infrastructure models of IaaS, Containers and PaaS (Serverless Cloud Services). We plan to continue our strong focus on serverless, and you will see more out-of-the-box integrations from Control-M to support the PaaS model. 

We recognise that enterprise orchestration is a team sport, which involves coordination across engineering, operations and business users. And, with this in mind, we plan to bring a user experience and interface that is persona based so that collaboration is frictionless. 

Specifically, within DataOps we are looking at the intersection of orchestration and data quality with a specific focus on making data quality a first-class citizen within application and data workflows. Stay tuned for more on this front!

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Tags: automation, BMC, data orchestration, DataOps