Trinity  Kub

Trinity Kub

1619227989

How to Parse and Clean Data using Pandas Library

Hello everyone and welcome. In this video, you will learn about how to parse and clean data using Pandas Library.

#pandas #python

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How to Parse and Clean Data using Pandas Library
Siphiwe  Nair

Siphiwe Nair

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

PANDAS: Most Used Functions in Data Science

Most useful functions for data preprocessing

When you get introduced to machine learning, the first step is to learn Python and the basic step of learning Python is to learn pandas library. We can install pandas library by pip install pandas. After installing we have to import pandas each time of the running session. The data used for example is from the UCI repository “https://archive.ics.uci.edu/ml/datasets/Heart+failure+clinical+records

  1. Read Data

2. Head and Tail

3. Shape, Size and Info

4. isna

#pandas: most used functions in data science #pandas #data science #function #used python data #most used functions in data science

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

Gerhard  Brink

Gerhard Brink

1624699032

Introduction to Data Libraries for Small Data Science Teams

At smaller companies access to and control of data is one of the biggest challenges faced by data analysts and data scientists. The same is true at larger companies when an analytics team is forced to navigate bureaucracy, cybersecurity and over-taxed IT, rather than benefit from a team of data engineers dedicated to collecting and making good data available.

Creative, persistent analysts find ways to get access to at least some of this data. Through a combination of daily processes to save email attachments, run database queries, and copy and paste from internal web pages one might build up a mighty collection of data sets on a personal computer or in a team shared drive or even a database.

But this solution does not scale well, and is rarely documented and understood by others who could take it over if a particular analyst moves on to a different role or company. In addition, it is a nightmare to maintain. One may spend a significant part of each day executing these processes and troubleshooting failures; there may be little time to actually use this data!

I lived this for years at different companies. We found ways to be effective but data management took up way too much of our time and energy. Often, we did not have the data we needed to answer a question. I continued to learn from the ingenuity of others and my own trial and error, which led me to the theoretical framework that I will present in this blog series: building a self-managed data library.

A data library is _not _a data warehousedata lake, or any other formal BI architecture. It does not require any particular technology or skill set (coding will not be required but it will greatly increase the speed at which you can build and the degree of automation possible). So what is a data library and how can a small data analytics team use it to overcome the challenges I’ve described?

#big data #cloud & devops #data libraries #small data science teams #introduction to data libraries for small data science teams #data science

Data Lake and Data Mesh Use Cases

As data mesh advocates come to suggest that the data mesh should replace the monolithic, centralized data lake, I wanted to check in with Dipti Borkar, co-founder and Chief Product Officer at Ahana. Dipti has been a tremendous resource for me over the years as she has held leadership positions at Couchbase, Kinetica, and Alluxio.

Definitions

  • A data lake is a concept consisting of a collection of storage instances of various data assets. These assets are stored in a near-exact, or even exact, copy of the resource format and in addition to the originating data stores.
  • A data mesh is a type of data platform architecture that embraces the ubiquity of data in the enterprise by leveraging a domain-oriented, self-serve design. Mesh is an abstraction layer that sits atop data sources and provides access.

According to Dipti, while data lakes and data mesh both have use cases they work well for, data mesh can’t replace the data lake unless all data sources are created equal — and for many, that’s not the case.

Data Sources

All data sources are not equal. There are different dimensions of data:

  • Amount of data being stored
  • Importance of the data
  • Type of data
  • Type of analysis to be supported
  • Longevity of the data being stored
  • Cost of managing and processing the data

Each data source has its purpose. Some are built for fast access for small amounts of data, some are meant for real transactions, some are meant for data that applications need, and some are meant for getting insights on large amounts of data.

AWS S3

Things changed when AWS commoditized the storage layer with the AWS S3 object-store 15 years ago. Given the ubiquity and affordability of S3 and other cloud storage, companies are moving most of this data to cloud object stores and building data lakes, where it can be analyzed in many different ways.

Because of the low cost, enterprises can store all of their data — enterprise, third-party, IoT, and streaming — into an S3 data lake. However, the data cannot be processed there. You need engines on top like Hive, Presto, and Spark to process it. Hadoop tried to do this with limited success. Presto and Spark have solved the SQL in S3 query problem.

#big data #big data analytics #data lake #data lake and data mesh #data lake #data mesh