Carroll  Klein

Carroll Klein


How to Encode and Decode Your Data in Rust

Encoding is the process of converting data from one form to another. Decoding means exactly the same thing. Though it’s often defined as a process, encoding also refers to a particular form of data (character encoding or media encoding).

Character encoding/decoding is particularly crucial in programming because computers recognize only binary data. It’s how we translate a sequence of characters (letters, numbers, symbols, punctuations, etc.) into a specialized format to help us speak the computer’s language and understand what it says back.

In this guide, we’ll demonstrate how to encode and decode your data in Rust.

Encoding and decoding in Rust

If you think encoding and decoding sound like a drag, you’re not alone. There are many edge cases and the process can be quite complex.

Fortunately, in Rust, as in many other programming languages, encoding and decoding are handled by modules that have been thoroughly tested against most of these edge cases. Efficient encoding and decoding libraries are especially critical for a language as close to the machine as Rust.

Encoding in Rust is relatively simple. Though it doesn’t come in the core Rust package, the few solutions developed by the community handle the job quite well. These tools enable you to send a string of characters to encode or decode through a function and receive the pursued result (encoded or decoded string).

base64 Rust library

base64 is designed to encode and decode to/from base64 as fast and precisely as possible. As its name suggests, it works only with base64. It literally has two transforming functions — encode() and decode() — along with configuration functions to help you shape the way it decodes and encodes.

extern crate base64;
use base64::{encode};
fn main() {
    let a = "hello world";
    println!("{}", encode(a)); // -> aGVsbG8gd29ybGQ=

Believe it or not, base64 is actually a first-class necessity when dealing with binary files on your computer. Base64 is commonly used to encode binary data (images, sound files etc), which are used in everything we share on the web, from emails attachment to saving files in our databases.

For a much deeper dive, head to base64 guru.


rust-encoding supports a massive amount of character encoding (all those supported by the WHATWG standards). Quite uniquely, it handles errors by replacing the error received while treating a string by a specified value. When an error is detected, you can use the strict mode to completely stop execution=.

Encoding’s encode and decode methods convert a String to Vec<u8> and vice versa. Since there’s support for a lot of encoding types, the library ships with two ways to get your encoding:

  1. Encoding::all to which you attach the encoding you’ll use for the encoding process. All the unused encoding types are discarded from the binary
  2. encoding::label, which captures the encoding based on the label given and returns the static encoding type, resulting in a bigger binary
use encoding::{Encoding, EncoderTrap};
use encoding::all::ISO_8859_1; // use with all
use encoding::label::encoding_from_whatwg_label; // use with label

assert_eq!(ISO_8859_1.encode("caf\u{e9}", EncoderTrap::Strict),
let euckr = encoding_from_whatwg_label("euc-kr").unwrap();
assert_eq!(, "windows-949");

rust-encoding is one of the top downloaded libraries (3.5k/week) even though it hasn’t been updated in four years. It’s safe to say that it’s extremely stable and robust.

#rust #programming #developer

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How to Encode and Decode Your Data in Rust
Siphiwe  Nair

Siphiwe Nair


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Cyrus  Kreiger

Cyrus Kreiger


How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Macey  Kling

Macey Kling


Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

  • Industrial metrology for quality assurance.
  • 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data

Uriah  Dietrich

Uriah Dietrich


What Is ETLT? Merging the Best of ETL and ELT Into a Single ETLT Data Integration Strategy

Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:

  • ETL is valuable when it comes to data quality, data security, and data compliance. It can also save money on data warehousing costs. However, ETL is slow when ingesting unstructured data, and it can lack flexibility.
  • ELT is fast when ingesting large amounts of raw, unstructured data. It also brings flexibility to your data integration and data analytics strategies. However, ELT sacrifices data quality, security, and compliance in many cases.

Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.

#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data