Troy  Marvin

Troy Marvin

1593307620

Data driven testing using karate DSL

In the previous blog, we saw that we can integrate our test cases with Jenkins. Now, in this blog, we will see how we can achieve data-driven testing using kara

#api testing #testing #qa #dsl #data

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Data driven testing using karate DSL
 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

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

Uriah  Dietrich

Uriah Dietrich

1618521240

Only Data-Minded Marketers and Market-Minded Developers Can Achieve Data Driven Marketing

Using data as a part of your marketing plan can have a tremendous impact on your overall results, which is why data-driven marketing has become the standard for many agencies.

However, data-driven marketing may require many businesses to rethink the way they work, especially when it comes to cooperation between their various teams.

You may have heard about the concept of collaboration and automating processes before - something referred to as webops. Now an increasing number of companies are throwing marketing into the mix.

Among the most important factors is a close working relationship between marketing and web development teams if a business wants to make the most of data-driven marketing.

#data-driven #data-driven-marketing #web-development #marketing-data-science #teamwork #data-driven-development #data-driven-decision-making #webops

Troy  Marvin

Troy Marvin

1593307620

Data driven testing using karate DSL

In the previous blog, we saw that we can integrate our test cases with Jenkins. Now, in this blog, we will see how we can achieve data-driven testing using kara

#api testing #testing #qa #dsl #data

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