Introduction to Solving Data Problems with UNIX

Description
UNIX, Linux, and their variants are wonderful operating systems, with great capabilities of doing data processing. But they are often ignored when people think about solving data problems, or in general, Data Science. This short course provides a hands-on approach to using UNIX/Linux for solving various data problems from sorting and searching to extracting and manipulating information. And all of these without writing any code! That’s right. With just a few commands of UNIX, and appropriate combinations of them, one could solve many data problems within seconds without writing thousands of lines or code or spending hours and days.

Basic knowledge
This course is for beginners, but it helps to have some basic understanding of operating systems (e.g., finding and launching programs, installing and configuring apps), as well as some basic knowledge of any programming. The course also assumes that you have a way to connect to a UNIX/Linux system. Alternatively, you could install a free utility on your computer that enables you to work on a UNIX-like environment (not needed for a Mac or a Linux desktop)
What will you learn
Remotely logging into a UNIX/Linux machine
File transfer to a remote machine
A basic understanding of some of the common tools useful for doing programming
Several basic UNIX commands and utilities
Solving several kinds of data problems using UNIX
To continue:

#developmenttool #data-problems #unix #solving-data-problems-with-unix

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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

Introduction to Solving Data Problems with UNIX

Description
UNIX, Linux, and their variants are wonderful operating systems, with great capabilities of doing data processing. But they are often ignored when people think about solving data problems, or in general, Data Science. This short course provides a hands-on approach to using UNIX/Linux for solving various data problems from sorting and searching to extracting and manipulating information. And all of these without writing any code! That’s right. With just a few commands of UNIX, and appropriate combinations of them, one could solve many data problems within seconds without writing thousands of lines or code or spending hours and days.

Basic knowledge
This course is for beginners, but it helps to have some basic understanding of operating systems (e.g., finding and launching programs, installing and configuring apps), as well as some basic knowledge of any programming. The course also assumes that you have a way to connect to a UNIX/Linux system. Alternatively, you could install a free utility on your computer that enables you to work on a UNIX-like environment (not needed for a Mac or a Linux desktop)
What will you learn
Remotely logging into a UNIX/Linux machine
File transfer to a remote machine
A basic understanding of some of the common tools useful for doing programming
Several basic UNIX commands and utilities
Solving several kinds of data problems using UNIX
To continue:

#developmenttool #data-problems #unix #solving-data-problems-with-unix

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).

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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

How to do Problem Solving as a Developer

In this video, I will be talking about problem-solving as a developer.

#problem solving skills #problem solving how to #problem solving strategies #problem solving #developer

An Introduction to Data Connectors: Your First Step to Data Analytics

Modern analytics teams are hungry for data. They are generating incredible insights that make their organizations smarter and are emphasizing the need for data-driven decision making across the board. However, data comes in many shapes and forms and is often siloed away. What actually makes the work of analytics teams possible is the aggregation of data from a variety of sources into a single location where it is easy to query and transform. And, of course, this data needs to be accurate and up-to-date at all times.

Let’s take an example. Maybe you’re trying to understand how COVID-19 is impacting your churn rates, so you can plan your sales and marketing spends appropriately in 2021. For this, you need to extract and combine data from a few different sources:

• MySQL database that details all the interactions your users are having with your product
• Salesforce account that contains the latest information about your current and prospective customers
• Zendesk account that has all support tickets raised by your customers

#data-analytics #data-science #data-engineering #data #data-warehouse #snowflake #data-connector #machine-learning