Sid  Schuppe

Sid Schuppe

1618393260

Demystifying Data Observability

As companies ingest more and more data and data pipelines become increasingly complex, the opportunity for error grows, manifesting in everything from a broken dashboard to a null value.

Here are 3 tell-tale signs your data engineering team could benefit from data observability, the modern data stack’s newest layer._

A customer recently asked me: “how do I know if I can trust my data?”

When I was the VP of Customer Success at Gainsight, this question came up a lot. Every data organization I worked with was different, with their own service level agreements (SLAs), security requirements, and KPIs for what “accurate, high quality data” looked like. Between all of these data teams, however, a common theme emerged: the need for a better approach to monitoring the reliability of data and eliminating data downtime.

#data-analytics #data #data-science #data-engineering #data-observability

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Demystifying Data Observability
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

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

Sid  Schuppe

Sid Schuppe

1618393260

Demystifying Data Observability

As companies ingest more and more data and data pipelines become increasingly complex, the opportunity for error grows, manifesting in everything from a broken dashboard to a null value.

Here are 3 tell-tale signs your data engineering team could benefit from data observability, the modern data stack’s newest layer._

A customer recently asked me: “how do I know if I can trust my data?”

When I was the VP of Customer Success at Gainsight, this question came up a lot. Every data organization I worked with was different, with their own service level agreements (SLAs), security requirements, and KPIs for what “accurate, high quality data” looked like. Between all of these data teams, however, a common theme emerged: the need for a better approach to monitoring the reliability of data and eliminating data downtime.

#data-analytics #data #data-science #data-engineering #data-observability

Uriah  Dietrich

Uriah Dietrich

1618137000

Data Observability: How to Fix Data Quality at Scale

Companies spend upwards of $15 million annually tackling data downtime, in other words, periods of time where data is missing, broken, or otherwise erroneous, and 1 in 5 companies have lost a customer due to incomplete or inaccurate data.
Fortunately, there’s hope in the next frontier of data: observability. Here’s how data engineers and BI analysts at Yotpo, a global eCommerce company, increases cost savings, collaboration, and productivity with data observability at scale.
Yotpo works with eCommerce companies across the world to help them accelerate online revenue growth through reviews, visual marketing, loyalty and referral programs, and SMS marketing.
For Yoav Kamin, Director of Business Performance, and Doron Porat, Data Engineering Team Leader, having consistently accurate and reliable data is foundational to the success of this mission.

#data-analysis #data-observability #data-engineering #data-quality #data

Uriah  Dietrich

Uriah Dietrich

1618037160

Data Observability: How to Prevent Broken Data Pipelines at Scale

Companies spend upwards of $15 million annually tackling data downtime, in other words, periods of time where data is missing, broken, or otherwise erroneous, and over 88 percent of U.S. businesses have lost money as a result of data quality issues.
Fortunately, there’s hope in the next frontier of data engineering: data observability. Here’s how the data engineering team at Blinkist, a book-summarizing subscription service, increases cost savings, collaboration, and productivity with data observability at scale.
With over 16 million users worldwide, Blinkist helps time-strapped readers fit learning into their lives through their ebook subscription service.

#data-observability #data-engineering #data #data-quality #data-management