Data Processing In Rust With DataFusion (Arrow)

Rust is the most beloved language, according to StackOverflow, it is on the top of the list for four years! Data processing is getting simpler and faster with a framework like Apache Spark. However, the field of data processing is competitive. DataFusion (part of Arrow now) is one of the initial attempts of bringing data processing to the Rust. If you are interested in learning some aspects of data processing in Rust with DataFusion, I will show some code examples in Rust with DataFusion, as well as compare the query performance between DataFusion and Pandas.

DataFusion

Andy Grove created DataFusion, and he had some great articles about building modern distributed computing, for example, How To Build A Modern Distributed Compute Platform. The DataFusion project is not for the production environment yet, as Andy mentioned,

“This project is a great way to learn about building a query engine, but this is quite early and not usable for any real-world work just yet.”

The project was donated to the Apache Arrow project in February 2019, and more people start to contribute to the Arrow version of DataFusion.

DataFusion is an in-memory query engine that uses Apache Arrow as the memory model. It supports executing SQL queries against CSV and Parquet files as well as querying directly against in-memory data.

The project description may not deliver too much excitement here, but since the entire project is done in Rust, it provides you ideas about writing your analytics SQL in Rust. Additionally, you can bring DataFusion as a library to your Cargo file for your Rust project easily.

Initial Setup

To test run some code with DataFusion, first, we need to create a new Rust package

cargo new datafusion_test --bin

Then bring DataFusion as a dependency in Cargo.toml file

[dependencies]
arrow = "0.15.0"
datafusion = "0.15.0"

#rust #programming #data #data-engineering #data-science #data analysis

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Data Processing In Rust With DataFusion (Arrow)
 iOS App Dev

iOS App Dev

1620466520

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

Data Processing In Rust With DataFusion (Arrow)

Rust is the most beloved language, according to StackOverflow, it is on the top of the list for four years! Data processing is getting simpler and faster with a framework like Apache Spark. However, the field of data processing is competitive. DataFusion (part of Arrow now) is one of the initial attempts of bringing data processing to the Rust. If you are interested in learning some aspects of data processing in Rust with DataFusion, I will show some code examples in Rust with DataFusion, as well as compare the query performance between DataFusion and Pandas.

DataFusion

Andy Grove created DataFusion, and he had some great articles about building modern distributed computing, for example, How To Build A Modern Distributed Compute Platform. The DataFusion project is not for the production environment yet, as Andy mentioned,

“This project is a great way to learn about building a query engine, but this is quite early and not usable for any real-world work just yet.”

The project was donated to the Apache Arrow project in February 2019, and more people start to contribute to the Arrow version of DataFusion.

DataFusion is an in-memory query engine that uses Apache Arrow as the memory model. It supports executing SQL queries against CSV and Parquet files as well as querying directly against in-memory data.

The project description may not deliver too much excitement here, but since the entire project is done in Rust, it provides you ideas about writing your analytics SQL in Rust. Additionally, you can bring DataFusion as a library to your Cargo file for your Rust project easily.

Initial Setup

To test run some code with DataFusion, first, we need to create a new Rust package

cargo new datafusion_test --bin

Then bring DataFusion as a dependency in Cargo.toml file

[dependencies]
arrow = "0.15.0"
datafusion = "0.15.0"

#rust #programming #data #data-engineering #data-science #data analysis

Gerhard  Brink

Gerhard Brink

1620629020

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.

Introduction

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

 iOS App Dev

iOS App Dev

1622608260

Making Sense of Unbounded Data & Real-Time Processing Systems

Unbounded data refers to continuous, never-ending data streams with no beginning or end. They are made available over time. Anyone who wishes to act upon them can do without downloading them first.

As Martin Kleppmann stated in his famous book, unbounded data will never “complete” in any meaningful way.

“In reality, a lot of data is unbounded because it arrives gradually over time: your users produced data yesterday and today, and they will continue to produce more data tomorrow. Unless you go out of business, this process never ends, and so the dataset is never “complete” in any meaningful way.”

— Martin Kleppmann, Designing Data-Intensive Applications

Processing unbounded data requires an entirely different approach than its counterpart, batch processing. This article summarises the value of unbounded data and how you can build systems to harness the power of real-time data.

#stream-processing #software-architecture #event-driven-architecture #data-processing #data-analysis #big-data-processing #real-time-processing #data-storage

Sid  Schuppe

Sid Schuppe

1617955288

Benefits of Data Ingestion

In the last two decades, many businesses have had to change their models as business operations continue to complicate. The major challenge companies face today is that a large amount of data is generated from multiple data sources. So, data analytics have introduced filters to various data sources to detect this problem. They need analytics and business intelligence to access all their data sources to make better business decisions.

It is obvious that the company needs this data to make decisions based on predicted market trends, market forecasts, customer requirements, future needs, etc. But how do you get all your company data in one place to make a proper decision? Data ingestion consolidates your data and stores it in one place.

#big data #data access #data ingestion #data collection #batch processing #data access layer #data integration platform #automate data collection