Big Data

23 Guides Available

Manually Deploy an Apache Kafka Cluster on Akamai

Apache Kafka is a scalable open source, distributed system for managing real-time data streams. Kafka supports a wide range of applications, including those used for log aggregation, monitoring, and real-time analytics. Kafka is considered an industry standard for use cases such as data pipelines, event-driven architectures, and stream-processing applications.

Create a Realtime Data Stream with Apache Storm

Apache Storm is a distributed stream processing computation framework. It integrates with various systems and libraries, including Apache Kafka, Apache Cassandra, Redis, Amazon Kinesis, Kestrel, RabbitMQ/Advanced Message Queuing Protocol (AMQP), and Java Message Service (JMS).

How to Create a Message Stream with RabbitMQ

Version 3.9 of RabbitMQ introduced a new data structure called a stream. Streams and queues differ in how they work with data. A message stream is subscription-based, relying on a log file organized by topic for distribution. Meanwhile, a message queue is point-to-point. Other differences between streams and queues are described later, but the key distinction is how they manage data distribution. This difference determines the use cases in which each data structure is most effective.

How to Process Streams Using Kafka

This guide explores Apache Kafka and its ability to process data streams. Data sources are provided by applications using Kafka libraries. These applications push data to Kafka, which serializes and stores the data. Kafka then makes the logged data available to other applications written in various languages.

Getting Started With PyTorch Lightning

Lightning is a PyTorch module that works within a number of integrated development environments (IDEs). There are compelling reasons to familiarize yourself with it. It’s portable, enabling code usage across setups. Development can occur on a cheaper host before moving to a GPU, at which point PyTorch Lightning provides mechanisms that grant further savings.

Why You Should Use Apache Spark for Data Analytics

Within the growing field of data science, Apache Spark has established itself as a leading open source analytics engine. Spark includes components for SQL queries, machine learning, graphing, and stream processing. This guide provides some background on Spark and explains its many advantages and use cases.

An Introduction to the Rasa Framework for Automated Chats

Rasa is an open source machine learning framework that uses a story-driven approach for developing automated text and voice chat assistants. Rasa employs an efficient and approachable model for crafting and training chatbots, and the framework includes numerous points for integrating automated assistants.

The Most Common Cyber Attacks on Machine Learning Applications

Machine learning (ML) algorithms and models ingest large amounts of data and use pattern recognition to make predictions and adjustments based on that data. ML powers chatbots, product recommendation systems, self-driving cars, and assists in decision-making in the health and financial sectors. Due to the prevalence of tools and frameworks like TensorFlow and PyTorch, developers are now able to add ML to their applications with less effort. Before getting started with machine learning, you should be aware of the most common machine learning cyber attacks. When thinking about the security of your ML application, you must consider the following areas:

Deep Learning vs. Machine Learning: What’s the Difference?

Deep Learning (DL) is a subset of Machine Learning (ML), and both are subsets of Artificial Intelligence (AI). Many people refer to deep learning and machine learning as AI simply because few people understand the distinctions in the various subsets and types. Think of AI as the machine equivalent to the human brain, and DL and ML as different parts of that brain with deep learning being embedded within machine learning.

The History of Machine Learning: A Comprehensive Overview

Machine learning (ML) is a subset of artificial intelligence (AI) and it is fast becoming the very backbone of most software today. It exists in several different forms from chatbots on websites and digital assistants like Siri and Alexa, to algorithms driving social media like Facebook, and a variety of office software. Machine learning is so prevalent that it is difficult to list all the places where it digitally resides and all the tasks it performs.

Install Anaconda on Linux CentOS

Anaconda is a distribution of the Python and R programming languages. It is a curated bundle of specific language interpreters and add-ons. Anaconda’s user base is primarily from the scientific computing, data analytics, and machine learning communities.

An Overview of Deep Learning Frameworks

What is Deep Learning?

Artificial Intelligence (AI) is an exceptionally broad term for a multitude of methods and practices to simulate or mimic human behavior. This includes machine learning (ML), natural language processing (NLP), language synthesis, computer vision, robotics, sensor analysis, and more.

An Introduction to Apache Kafka

Apache Kafka, often known simply as Kafka, is a popular open-source platform for stream management and processing. Kafka is structured around the concept of an event. External agents, independently and asynchronously, send and receive event notifications to and from Kafka. Kafka accepts a continuous stream of events from multiple clients, stores them, and potentially forwards them to a second set of clients for further processing. It is flexible, robust, reliable, self-contained, and offers low latency along with high throughput. LinkedIn originally developed Kafka, but the Apache Software Foundation offers the current open-source iteration.

Install and Configure Apache Kafka on Ubuntu

Installing Anaconda on Ubuntu

Install Anaconda on Ubuntu

Installing Anaconda on Linode (or other Linux system) using the CLI may look daunting, but don’t let it scare you. It is possible to install it without using a graphical environment.

An Introduction to Machine Learning: Training and Inference

Machine learning (ML) has been around conceptually since 1959, when Arthur Samuel, a pioneer in the field of computer gaming and artificial intelligence, coined the term. Samuel said that machine learning “gives computers the ability to learn without being explicitly programmed”. While at IBM he wrote a program to play Checkers, which became the first known self-learning program.

Installing TensorFlow on Ubuntu 20.04

TensorFlow is an open-source software library used for machine learning and to train deep neural networks. Google developed TensorFlow for both research and production use, but it is now released under the Apache license. It is available for many operating systems, including the most common Linux distributions. For learning purposes, it is best to install TensorFlow in a Python virtual environment. TensorFlow is considered a good choice for those who are new to machine learning.

Create Connections and Variables in Apache Airflow

What is Apache Airflow?

Airflow is an open source platform that you can use to automate, orchestrate, and monitor workflows and data pipelines. One of Airflow’s greatest features is that you can create and execute workflows with code. When you use workflows that are powered by code, you can version control, collaborate on, and debug your workflows.

Scrape a Website with Beautiful Soup

How to Move Your Machine Learning Model to Production

Installing a Jupyter Notebook on a Linode Behind an Apache Reverse Proxy

Jupyter Notebook is an interactive, enhanced shell that can be run within a web browser. Notebook is popular among data scientists, and supports inline rendering of figures, exporting to a variety of formats, and LaTeX for mathematical notation. This guide aims to configure on a Linode a public Jupyter Notebook server that will facilitate remote access to your computation needs using Apache as a reverse proxy.

How to Install and Configure a Redis Cluster on Ubuntu 16.04

Streaming Data Processing with Apache Storm

Apache Storm is a big data technology that enables software, data, and infrastructure engineers to process high velocity, high volume data in real time and extract useful information. Any project that involves processing high velocity data streams in real time can benefit from it.

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