Best Practises In Data Cleaning That Data Analysts Should Know

Data cleaning is one of the most crucial steps to ensure data quality and database integrity. It efficiently allows managing data while determining reliability while making decisions. As the regulatory compliances are becoming more stringent and focused, ensuring high data quality is the need of the hour.

Read more: https://analyticsindiamag.com/best-practises-in-data-cleaning-that-data-analysts-should-know/

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Best Practises In Data Cleaning That Data Analysts Should Know
bindu singh

bindu singh

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You can become an air hostess if you meet certain criteria, such as a minimum educational level, an age limit, language ability, and physical characteristics.

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 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

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.

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Uriah  Dietrich

Uriah Dietrich

1618457700

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.

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Data Cleansing — Where to Start?

A guideline and checklist of what to do for Data Cleansing/Wrangling

It turns out that Data Scientists and Data Analysts will spend most of their time on data preprocessing and EDA rather than training a machine learning model. As one of the most important job, Data Cleansing is very important indeed.

We all know that we need to clean the data. I guess most people know that. But where to start? In this article, I will provide a generic guide/checklist. So, once we start on a new dataset, we can start the Data Cleansing as such.

Methodology (C-R-A-I)

Image for post

Photo by geralt on Pixabay

If we ask ourselves “why do we need to clean the data?”, I think it is obvious that it is because we want our data to follow some standards in order to be fed into some algorithm or visualised on a consistent scale. Therefore, let’s firstly summarise what are the “standards” that we want our data to have.

Here, I summarised 4 major criteria/standards that a cleansed dataset should have. I would call it “CRAI”.

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