Booked: Should You Allow Cab Services To Use Your Data

All your favourite apps work the way they do because they rely heavily on personalisation. Algorithms devour the data that users provide and leverage the behavioural patterns of users to offer ‘5-star’ experience. There is nothing with the internet knowing your favourite movie, but how willing are you to give up details of your most visited place? Ride-hailing services use location data which in turn can reveal personal habits and preferences. Riders might not be keen to share the exact location of their origin and/or destination.

Read more:
https://analyticsindiamag.com/cab-ride-uber-ola-services-trip-data-privacy/

#ola #uber #dataprivacy #dataprotection #datacollection #cybersecurity

What is GEEK

Buddha Community

Booked: Should You Allow Cab Services To Use Your Data
 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

Big Data Consulting Services | Big Data Development Experts USA

Big Data Consulting Services

Traditional data processing application has limitations of its own in terms of processing the large chunk of complex data and this is where the big data processing application comes into play. Big data processing app can easily process complex and large information with their advanced capabilities.

Want to develop a Big Data Processing Application?

WebClues Infotech with its years of experience and serving 350+ clients since our inception is the agency to trust for the Big Data Processing Application development services. With a team that is skilled in the latest technologies, there can be no one better for fulfilling your development requirements.

Want to know more about our Big Data Processing App development services?

Visit: https://www.webcluesinfotech.com/big-data-solutions/

Share your requirements https://www.webcluesinfotech.com/contact-us/

View Portfolio https://www.webcluesinfotech.com/portfolio/

#big data consulting services #big data development experts usa #big data analytics services #big data services #best big data analytics solution provider #big data services and consulting

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

Ruth  Nabimanya

Ruth Nabimanya

1624850863

Azure Data Catalog: A Quick Introduction to Data Handling Service Around

What is Azure Data Catalog?

Azure Data Catalog is a Data Catalog cloud service of Microsoft using a crowdsourced approach. It provides an inventory of data used for discovering and understanding the data sources. Microsoft Azure is a Software as a Service (SaaS) application.

“Build Confidence in Azure Data Catalog even having more than millions of accounts”

**Source: **Gartner, Inc

Azure Data Catalog enhances old investments’ performance, adding metadata and notation around the Azure environment’s data. It informs about the Data sources which we have discovered or which we already have. It expresses documentation and describes the schema of the data source. The data source location and a copy of the metadata are present in the Azure Data Catalog. The user can access it easily when needed, and the indexing of metadata helps discover data through a search.### What is Azure Data Catalog?

Azure Data Catalog is a Data Catalog cloud service of Microsoft using a crowdsourced approach. It provides an inventory of data used for discovering and understanding the data sources. Microsoft Azure is a Software as a Service (SaaS) application.

“Build Confidence in Azure Data Catalog even having more than millions of accounts”

**Source: **Gartner, Inc

Azure Data Catalog enhances old investments’ performance, adding metadata and notation around the Azure environment’s data. It informs about the Data sources which we have discovered or which we already have. It expresses documentation and describes the schema of the data source. The data source location and a copy of the metadata are present in the Azure Data Catalog. The user can access it easily when needed, and the indexing of metadata helps discover data through a search.

#big data engineering #blogs #azure data catalog: a quick introduction to data handling service around #azure data catalog #data handling service around #service

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