Chet  Lubowitz

Chet Lubowitz

1598726280

How Automated Data Validation using Pandera Made Me More Productive!

ata is called as the _new oil _of 21st Century. It is very important to juggle with the data to extract and use the correct information to solve our problems. Working with data can be exciting and sometimes tedious for people. As it is rightly said, “Data Scientists spend 80% of their time cleaning the data”. Being a part of that pack, I go through the same process when encountering a new dataset. The same activity is not limited to until the Machine Learning(ML) system is implemented and deployed to production. When generating predictions in real time, the data might change due to unintuitive and unforseeable circumstances like error due to human interference, wrong data submitted, a new trend in data, problem while recording data, and many more. A simple ML system with multiple steps involved looks like shown in the diagram below:

Image for post

Image created by the author (Pratik Gandhi)

This needs to be slightly shifted by introducing or labeling another component explicitly, after data preparation and before feature engineering we name as Data Validation:

Image for post

Image created by the author (Pratik Gandhi)

The article is focused on why data validation is important and how can one use different strategies to seemlessly integrate it in their pipeline. After some work, I learnt how to implement scripts that would do data validation to save some of the time. Above that, I **automated them **using some of the pre-built packages, stepping up my game!

Hear my Story!

Image for post

Photo by Road Trip with Raj on Unsplash

Almost** 85%** of projects will not make it to production as per Gartner. Machine Learning (ML) Pipelines usually face several hiccups when pushed in production. One of the major issues I have quite often experienced is the compromise of data quality. Spending multiple hours of a day, several times a month maybe, and figuring out that the data that came through was unacceptable because of some reason can be quite relieving but frustrating at the same time. Many reasons can contribute that leads to data type getting changed like, text getting introduced instead of an integer, an integer was on outlier (probably 10 times higher) or an entire specific column was not received in the data feed, to mention a few. That is why adding this extra step is so important. Validating manually can take some extra effort and time. Making it automated(to an extent) could reduce the burden of the Data Science team. There are some major benefits I see by integrating an automated data validation in the pipeline:

#editors-pick #data-science #data #machine-learning #data-validation

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How Automated Data Validation using Pandera Made Me More Productive!
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

Gerhard  Brink

Gerhard Brink

1622622360

Data Validation in Excel

Data Validation in Excel

In this tutorial, let’s discuss what data validation is and how it can be implemented in MS-Excel. Let’s start!!!

What Is Data Validation in Excel?

Data Validation is one of the features in MS-Excel which helps in maintaining the consistency of the data in the spreadsheet. It controls the type of data that can enter in the data validated cells.

Data Validation in MS Excel

Now, let’s have a look at how data validation works and how to implement it in the worksheet:

To apply data validation for the cells, then follow the steps.

1: Choose to which all cells the validation of data should work.

2: Click on the DATA tab.

3: Go to the Data Validation option.

4: Choose the drop down option in it and click on the Data Validation.

data validation in Excel

Once you click on the data validation menu from the ribbon, a box appears with the list of data validation criteria, Input message and error message.

Let’s first understand, what is an input message and error message?

Once, the user clicks the cell, the input message appears in a small box near the cell.

If the user violates the condition of that particular cell, then the error message pops up in a box in the spreadsheet.

The advantage of both the messages is that the input and as well as the error message guide the user about how to fill the cells. Both the messages are customizable also.

Let us have a look at how to set it up and how it works with a sample

#ms excel tutorials #circle invalid data in excel #clear validation circles in excel #custom data validation in excel #data validation in excel #limitation in data validation in excel #setting up error message in excel #setting up input message in excel #troubleshooting formulas in excel #validate data in excel

Chet  Lubowitz

Chet Lubowitz

1598726280

How Automated Data Validation using Pandera Made Me More Productive!

ata is called as the _new oil _of 21st Century. It is very important to juggle with the data to extract and use the correct information to solve our problems. Working with data can be exciting and sometimes tedious for people. As it is rightly said, “Data Scientists spend 80% of their time cleaning the data”. Being a part of that pack, I go through the same process when encountering a new dataset. The same activity is not limited to until the Machine Learning(ML) system is implemented and deployed to production. When generating predictions in real time, the data might change due to unintuitive and unforseeable circumstances like error due to human interference, wrong data submitted, a new trend in data, problem while recording data, and many more. A simple ML system with multiple steps involved looks like shown in the diagram below:

Image for post

Image created by the author (Pratik Gandhi)

This needs to be slightly shifted by introducing or labeling another component explicitly, after data preparation and before feature engineering we name as Data Validation:

Image for post

Image created by the author (Pratik Gandhi)

The article is focused on why data validation is important and how can one use different strategies to seemlessly integrate it in their pipeline. After some work, I learnt how to implement scripts that would do data validation to save some of the time. Above that, I **automated them **using some of the pre-built packages, stepping up my game!

Hear my Story!

Image for post

Photo by Road Trip with Raj on Unsplash

Almost** 85%** of projects will not make it to production as per Gartner. Machine Learning (ML) Pipelines usually face several hiccups when pushed in production. One of the major issues I have quite often experienced is the compromise of data quality. Spending multiple hours of a day, several times a month maybe, and figuring out that the data that came through was unacceptable because of some reason can be quite relieving but frustrating at the same time. Many reasons can contribute that leads to data type getting changed like, text getting introduced instead of an integer, an integer was on outlier (probably 10 times higher) or an entire specific column was not received in the data feed, to mention a few. That is why adding this extra step is so important. Validating manually can take some extra effort and time. Making it automated(to an extent) could reduce the burden of the Data Science team. There are some major benefits I see by integrating an automated data validation in the pipeline:

#editors-pick #data-science #data #machine-learning #data-validation

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

Virgil  Hagenes

Virgil Hagenes

1602702000

Data Quality Testing Skills Needed For Data Integration Projects

The impulse to cut project costs is often strong, especially in the final delivery phase of data integration and data migration projects. At this late phase of the project, a common mistake is to delegate testing responsibilities to resources with limited business and data testing skills.

Data integrations are at the core of data warehousing, data migration, data synchronization, and data consolidation projects.

In the past, most data integration projects involved data stored in databases. Today, it’s essential for organizations to also integrate their database or structured data with data from documents, e-mails, log files, websites, social media, audio, and video files.

Using data warehousing as an example, Figure 1 illustrates the primary checkpoints (testing points) in an end-to-end data quality testing process. Shown are points at which data (as it’s extracted, transformed, aggregated, consolidated, etc.) should be verified – that is, extracting source data, transforming source data for loads into target databases, aggregating data for loads into data marts, and more.

Only after data owners and all other stakeholders confirm that data integration was successful can the whole process be considered complete and ready for production.

#big data #data integration #data governance #data validation #data accuracy #data warehouse testing #etl testing #data integrations