Dask vs Vaex: Experience of a Data Point in Large Data Processing

Hello there! Nice to meet you! 😄 I’m Data N (you can call me N) and today, I would like to share my experience as a data point working with my new managers, Dask and Vaex, as well as some tips to have a good working relationship with them (wink).

Background Story

The background story goes like this… Recently, our company had a little restructuring and our ex-manager, Pandas 🐼, was taken over by two new hires. The official reason given was that Pandas moved on to new opportunities but all of us insiders knew what happened.

Well, the truth is that the top level management was not pleased with Pandas’ performance lately. Our company had grown quickly and business increased exponentially. Pandas was initially doing great but gradually find himself unable to cope with increasing data. When the full truckload of us data points arrives, we prove to be too much for Pandas to cope. Usually, we will sit in a large warehouse called hard disk, but when we need to be processed, there’s this temporary storage room called Random-Access Memory (a.k.a. RAM) where we will be transported to for further processing. Here’s where the problem lies: there’s not enough space for all of us to fit into RAM.

#dask #data-processing #vaex #machine-learning #data-science

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Dask vs Vaex: Experience of a Data Point in Large Data Processing

Dask vs Vaex: Experience of a Data Point in Large Data Processing

Hello there! Nice to meet you! 😄 I’m Data N (you can call me N) and today, I would like to share my experience as a data point working with my new managers, Dask and Vaex, as well as some tips to have a good working relationship with them (wink).

Background Story

The background story goes like this… Recently, our company had a little restructuring and our ex-manager, Pandas 🐼, was taken over by two new hires. The official reason given was that Pandas moved on to new opportunities but all of us insiders knew what happened.

Well, the truth is that the top level management was not pleased with Pandas’ performance lately. Our company had grown quickly and business increased exponentially. Pandas was initially doing great but gradually find himself unable to cope with increasing data. When the full truckload of us data points arrives, we prove to be too much for Pandas to cope. Usually, we will sit in a large warehouse called hard disk, but when we need to be processed, there’s this temporary storage room called Random-Access Memory (a.k.a. RAM) where we will be transported to for further processing. Here’s where the problem lies: there’s not enough space for all of us to fit into RAM.

#dask #data-processing #vaex #machine-learning #data-science

 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

Gerhard  Brink

Gerhard Brink

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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

Database Vs Data Warehouse Vs Data Lake: A Simple Explanation

Databases store data in a structured form. The structure makes it possible to find and edit data. With their structured structure, databases are used for data management, data storage, data evaluation, and targeted processing of data.
In this sense, data is all information that is to be saved and later reused in various contexts. These can be date and time values, texts, addresses, numbers, but also pictures. The data should be able to be evaluated and processed later.

The amount of data the database could store is limited, so enterprise companies tend to use data warehouses, which are versions for huge streams of data.

#data-warehouse #data-lake #cloud-data-warehouse #what-is-aws-data-lake #data-science #data-analytics #database #big-data #web-monetization

 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