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Cloud

Indestructible Storage in the Cloud with Apache Bookkeeper

At Salesforce, we required a storage system that could work with two kinds of streams, one stream for write-ahead logs and one for data. But we have competing requirements from both of the streams. Being the pioneers in cloud computing, we also required our storage system to be cloud-aware as the requirements of availability and durability are ever more increasing.

Anup Ghatage
on Apr 28, 2021
AI, ML & Data Engineering

The Future of Data Engineering

Chris Riccomini examines the current and future states of the art in data pipelines, data streaming, and data warehousing. He presents a six-stage evolution that data ecosystems follow, from a simple monolith to a complex data-microwarehouse architecture as the data engineers who manage them solve problems and clarify their roles as infrastructure engineers, rather than data stewards.

Chris Riccomini
on Feb 16, 2021 1
AI, ML & Data Engineering

How Apache Pulsar is Helping Iterable Scale its Customer Engagement Platform

In this article, author Greg Methvin discusses his experience implementing a distributed messaging platform based on Apache Pulsar.

Greg Methvin
on Nov 30, 2020
AI, ML & Data Engineering

Beyond the Database, and beyond the Stream Processor: What's the Next Step for Data Management?

Databases have been around forever with the same shape: you make a request to your data and then you receive an answer. Now, stream processors came along with a different approach: data isn’t locked up, it is in motion. Understand how stream processors and databases relate and why there is an emerging new category of databases that focus on data that stays in place as well as data that moves.

Ben Stopford
on Nov 16, 2020 1
Architecture & Design

Real Time APIs in the Context of Apache Kafka

Events offer a Goldilocks-style approach in which real-time APIs can be used as the foundation for applications which is flexible yet performant; loosely-coupled yet efficient. Apache Kafka offers a scalable event streaming platform with which you can build applications around the powerful concept of events.

Robin Moffatt
on Oct 16, 2020
Architecture & Design

The Challenges of Building a Reliable Real-Time Event-Driven Ecosystem

Globally, there is an increasing appetite for data delivered in real time; we are witnessing the emergence of the real time API. When it comes to event-driven APIs engineers can choose between multiple different protocols. In addition to choosing a protocol, engineers also have to think about subscription models, too: server-initiated (push-based) or client-initiated (pull-based).

Matthew O'Riordan
on Jul 30, 2020
AI, ML & Data Engineering

Applied Probability - Counting Large Set of Unstructured Events with Theta Sketches

In this article, author Ronen Cohen discusses the solution to processing the event data using Theta Sketches and technologies like HBase and Kafka.

Ronen Cohen
on Jun 29, 2020
Cloud

Is Edge Computing a Thing?

Edge Computing is definitely a thing, but the computing need not occur at the edge. Instead what is needed is an ability to compute (anywhere) on streaming data from large numbers of dynamically changing devices, in the edge environment. This in turn demands an architectural pattern for stateful, distributed computing.

Simon Crosby
on Apr 17, 2020
AI, ML & Data Engineering

The Kongo Problem: Building a Scalable IoT Application with Apache Kafka

In this article, author Paul Brebner discusses the best practices for developing IoT projects using Apache Kafka and Kafka Streams technologies and how to maximize Kafka scalability.

Paul Brebner
on Feb 15, 2020
AI, ML & Data Engineering

Rethinking Flink’s APIs for a Unified Data Processing Framework

Since its very early days, Apache Flink has followed the philosophy of taking a unified approach to batch and streaming. The core building block is the “continuous processing of unbounded data streams, with batch as a special, bounded set of those streams.” Recent updates to the Flink APIs include architectural designs by the community to support batch and streaming unification in Apache Flink.

Aljoscha Krettek
on Sep 16, 2019
AI, ML & Data Engineering

Azure Data Lake Analytics and U-SQL

In this article, the author shows how to use big data query and processing language U-SQL on Azure Data Lake Analytics platform. U-SQL combines the concepts and constructs both of SQL and C#. It combines the simplicity and declarative nature of SQL with the programmatic power of C# including rich types and expressions.

Aniruddha Chakrabarti
on Sep 13, 2019
AI, ML & Data Engineering

Stream Processing Anomaly Detection Using Yurita Framework

In this article, author Guy Gerson discusses the stream processing anomaly detection framework they developed by PayPal, called Yurita. The framework is based on Spark Structured Streaming.

Guy Gerson
on Jul 10, 2019