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AI, ML & Data Engineering

DynamoDB Data Transformation Safety: from Manual Toil to Automated and Open Source

Data transformation remains a continuous challenge in engineering and built upon manual toil. The open source utility Dynamo Data Transform was built to simplify and build safety and guardrails into data transformation for DynamoDB based systems––built upon a robust manual framework that was then automated and open sourced. This article discusses the challenges with Data Transformation.

Guy Braunstain
on Nov 23, 2022 1
AI, ML & Data Engineering

Create Your Distributed Database on Kubernetes with Existing Monolithic Databases

The next challenge for databases is to run them on Kubernetes to become cloud neutral. However, they are more difficult to manage than the application layer, since Kubernetes is designed for stateless applications. Apache ShardingSphere is the ecosystem to transform any database into a distributed database system and enhance it with sharding, elastic scaling, encryption features, and more.

Trista Pan
on Nov 16, 2022
AI, ML & Data Engineering

Apache DolphinScheduler in MLOps: Create Machine Learning Workflows Quickly

In this article, author discusses data pipeline and workflow scheduler Apache DolphinScheduler and how ML tasks are performed by Apache DolphinScheduler using Jupyter and MLflow components.

Zhou Jieguang
on Oct 14, 2022
AI, ML & Data Engineering

Migrating Netflix's Viewing History from Synchronous Request-Response to Async Events

In a web-based service, a slowdown in request processing can eventually make your service unavailable. Chances are, not all requests need to be processed right away. Some of them just need an acknowledgement of receipt. Have you ever asked yourself: “Would I benefit from asynchronous processing of requests? If so, how would I make such a change in a live, large-scale mission critical system?”

Sharma Podila
on Sep 12, 2022
AI, ML & Data Engineering

How to Migrate an Oracle Database to MySQL Using AWS Database Migration Service

Data migration efforts are typically taken up for database consolidation, cost considerations, or migrating on-prem databases to a cloud platform. In this article, author Deepak Vohra discusses the details of migrating a local database to MySQL database on the cloud, using AWS Database Migration Service.

Deepak Vohra
on Sep 07, 2022
AI, ML & Data Engineering

AutoML: the Promise vs. Reality According to Practitioners

Automation to improve machine learning projects comes from a noble goal, but true end-to-end automation is not available yet. As a collection of tools, AutoML capabilities have proven value but need to be vetted more thoroughly. Findings from a qualitative study of AutoML users suggest the future of automation for ML and AI rests in the ability for us to realize the potential of AutoMLOps.

Doris Xin
on Aug 29, 2022
Architecture & Design

Business Systems Integration is about to Get a Whole Lot Easier

A new breed of integration software is arising that syncs business data into a simplified data hub and then syncs that data to the destination system. The benefit of this integration pattern is that it reduces the number of manual transformations required (often to zero) and makes it easier to write manual transformations when you have to.

Doug Hudgeon
on Aug 24, 2022 2
AI, ML & Data Engineering

Streaming-First Infrastructure for Real-Time Machine Learning

This article covers the benefits of streaming-first infrastructure for two scenarios of real-time ML: online prediction, where a model can receive a request and make predictions as soon as the request arrives, and continual learning, when machine learning models are capable of continually adapting to change in data distributions in production.

Chip Huyen
on Aug 22, 2022
Architecture & Design

Creating a Secure Distributed Database Cluster Leveraging Your Existing Database Management System

The emergence of Big Data and data lakes doesn't necessarily mean the disappearance of the trusted relational database. The two can coexist, relational databases just need to adjust. For the transition we propose Database Plus, a new technology & concept applicable to any database, that answers these challenges and eliminates switching costs and vendor lock-in.

Trista Pan
on Aug 17, 2022
Java

Debezium and Quarkus: Change Data Capture Patterns to Avoid Dual-Writes Problems

It’s common in microservices to write data in two places, a database and then send the content to another microservice. One approach to tackle this problem is dual writes, but you may lose data because of concurrent writes. Debezium is an open-source project for change data capture using the log scanner approach to avoid dual writes and communicate persisted data correctly between services.

Alex Soto
on Aug 15, 2022 1
AI, ML & Data Engineering

AI, ML, and Data Engineering InfoQ Trends Report—August 2022

In this annual report, the InfoQ editors discuss the current state of AI, ML, and data engineering and what emerging trends you as a software engineer, architect, or data scientist should watch. We curate our discussions into a technology adoption curve with supporting commentary to help you understand how things are evolving.

Srini Penchikala Dr Einat Orr Rags Srinivas Roland Meertens Anthony Alford Daniel Dominguez
on Aug 02, 2022
.NET

Building Neural Networks with TensorFlow.NET

TensorFlow is an open-source framework developed by Google scientists and engineers for numerical computing. TensorFlow.NET is a library that provides a .NET Standard binding for TensorFlow. In this article, the author explains how to use Tensorflow.NET to build a neural network.

Robert Krzaczyński
on Jul 11, 2022