Rockset

JUNE 21, 2022

We’re excited to announce that Rockset’s new connector with Snowflake is now available and can increase cost efficiencies for customers building real-time analytics applications. From there, all the data from a Snowflake table will be ingested into a Rockset collection. Learn more at rockset.com.

Kafka 52

Kafka Data Warehouse BI Analytics Application 52

Towards Data Science

APRIL 26, 2023

An open-source implementation of a Data Lake with DuckDB and AWS Lambdas A duck in the cloud. Photo by László Glatz on Unsplash In this post we will show how to build a simple end-to-end application in the cloud on a serverless infrastructure. The cloud is better. The infrastructure often gets in the way though.

Engineering 67

Engineering Data Lake AWS BI 67

Rockset

MARCH 10, 2022

This means that updates are inserted into a different location or you’re forced to rewrite old and new data to store it properly. Immutable data stores have been useful in certain analytics scenarios. Analytical queries could be accelerated by caching heavily-accessed read-only data in RAM or SSDs.

Data Analytics 52

Data Analytics Data Warehouse Medical MySQL 52

Precisely

MARCH 10, 2023

With the right geocoding technology, accurate and standardized address data is entirely possible. This capability opens the door to a wide array of data analytics applications. The Rise of Cloud Analytics Data analytics has advanced rapidly over the past decade.

Datasets 59

Datasets Data Warehouse Big Data Data Analytics 59

Knowledge Hut

DECEMBER 26, 2023

The Microsoft BI platform features are real-time reporting, predictive analytics, and AI diagnostics. Oracle Analytics Cloud Oracle Analytics Cloud is one of the most powerful AI-driven systems available, which brings us back to genuine analytics. Essential features include Embed, Mashup, and analyze.

Business Intelligence 52

Business Intelligence BI Data Mining Data Analysis 52

Cloudera

MARCH 6, 2018

That’s what Cloudera SDX (Shared Data Experience) enables for our customers and why we’re so excited to introduce it today for Cloudera Altus. People are gravitating to the analytics services of the large public cloud providers because the “house-brand” offerings seem to be the easiest choice.

Cloud 40

Cloud Metadata Big Data Analytics Application 40

Rockset

OCTOBER 4, 2022

Streaming data feeds many real-time analytics applications, from logistics tracking to real-time personalization. Event streams, such as clickstreams, IoT data and other time series data, are common sources of data into these apps. Several vendors also offer cloud versions of ClickHouse.

MySQL 52

MySQL Kafka Aggregated Data Architecture 52

Cloudera

NOVEMBER 2, 2020

Users today are asking ever more from their data warehouse. As an example of this, in this post we look at Real Time Data Warehousing (RTDW), which is a category of use cases customers are building on Cloudera and which is becoming more and more common amongst our customers. What is Real Time Data Warehousing?

Data Warehouse 95

Data Warehouse Kafka Lambda Architecture Telecommunication 95

Rockset

FEBRUARY 2, 2022

Let’s explore five ways to run MongoDB analytics, along with the pros and cons of each method. 1 – Query MongoDB Directly The first and most direct approach is to run your analytical queries directly against MongoDB. 3 – Use a Data Warehouse Next, you can replicate your data to a data warehouse.

MongoDB 52

MongoDB NoSQL Data Warehouse BI 52

Cloudera

NOVEMBER 13, 2017

As the market moves toward cloud-based big data and analytics, three qualities emerge as vital for success. Cloud IaaS facilitates resource self-service provisioning, eliminating the hassles of procurement and deployment on-premises. Make sure any cloud-based analytics service meets these criteria.

Cloud 40

Cloud Big Data Metadata Machine Learning 40

Striim

AUGUST 31, 2023

Treating batch and streaming as separate pipelines for separate use cases drives up complexity, cost, and ultimately deters data teams from solving business problems that truly require data streaming architectures. Finally, kappa architectures are not suitable for all types of data processing tasks.

Data Integration 69

Data Integration Architecture Amazon Web Services Machine Learning 69

Edureka

AUGUST 23, 2024

The accuracy of decisions improves dramatically once you can use live data in real-time. The AWS training will prepare you to become a master of the cloud, storing, processing, and developing applications for the cloud data. As of 2024, about 73% of enterprises have deployed a hybrid cloud.

AWS 52

AWS Kafka Amazon Web Services Medical 52

Cloudera

SEPTEMBER 10, 2018

Uniquely, Cloudera’s machine learning and analytics platform have a fundamental characteristic called the Shared Data Experience (SDX) that provides just that. When transient cloud infrastructures are used to complement existing on-premises investments, establishing and capturing this data context is essential for success.

Data Warehouse 40

Data Warehouse Metadata Cloud Retail 40

Rockset

AUGUST 4, 2021

Organizations that depend on data for their success and survival need robust, scalable data architecture, typically employing a data warehouse for analytics needs. Snowflake is often their cloud-native data warehouse of choice. Snowflake provides a couple of ways to load data.

Data Ingestion 40

Data Ingestion Cloud Storage Data Warehouse Architecture 40

Monte Carlo

MAY 14, 2024

It enhances performance specifically for large-scale data processing tasks, offering advanced optimizations for superior data compression and fast data scans, essential in data warehousing and analytics applications.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Knowledge Hut

JUNE 26, 2023

Hundreds of datasets are available from these two cloud services, so you may practise your analytical skills without having to scrape data from an API. Source: Use Stack Overflow Data for Analytic Purposes 4. We can clean the data, convert the data, and aggregate the data using dbt so that it is ready for analysis.

Data Engineer 98

Data Engineer Data Engineering Coding Project 98

Rockset

MARCH 31, 2022

On top of that, I had to make that data available to our custom-built application via a secure RESTful endpoint with a less than one second response time. I was amazed that I could do all of that without having to initially move and transform the data. From there, the data could be ingested by any standard reporting tool.

MongoDB 52

MongoDB Data Architect Data Schemas SQL 52

Grouparoo

NOVEMBER 16, 2021

Transformation is the process of converting the data into the required standard format. Loading is the process of warehousing the data in an accessible location. The advent of cloud-based solutions has enabled a switch to Extract, Load, and Transform (ELT). To remove erroneous values and null entries that can disrupt processing.

Data Mining 52

Data Mining Raw Data ETL Tools Data 52

AltexSoft

OCTOBER 21, 2022

A single cluster can span across multiple data centers and cloud facilities. This allows for easy horizontal scaling — just add new servers or data centers to your existing infrastructure to handle more amount of data. cloud data warehouses — for example, Snowflake , Google BigQuery, and Amazon Redshift.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

Rockset

DECEMBER 9, 2019

The major benefit to having all the data in the same place means that it can be cleaned and transformed into a consistent format and then be joined together. This allows businesses to get a full 360 degree view of their data providing deeper insight and understanding.

Data Engineer 40

Data Engineer Data Engineering Engineering Raw Data 40

ProjectPro

AUGUST 9, 2016

Cognizant’s BIGFrame solution uses Hadoop to simplify migration of data and analytics applications to provide mainframe like performance at an economical cost of ownership over data warehouses.

Hadoop 52

Hadoop Insurance Cloud Computing Big Data 52

phData: Data Engineering

MAY 11, 2023

Practices centered on software engineering principles can create a barrier to entry for teams with skilled data wranglers looking to take their infrastructure to the next level with cloud-native tools like Matillion for the Snowflake Data Cloud. This is often called CURATED, or REPORT, or even DATA WAREHOUSE.

Coding 52

Coding Software Engineer Software Engineering Project 52

ProjectPro

SEPTEMBER 6, 2021

Are you confused about choosing the best cloud platform for your next data engineering project ? AWS vs. GCP blog compares the two major cloud platforms to help you choose the best one. So, are you ready to explore the differences between two cloud giants, AWS vs. google cloud? Let’s get started!

AWS 52

AWS Amazon Web Services Google Cloud Cloud Storage 52

ProjectPro

MARCH 1, 2017

(Source : [link] ) BlueTalon Enables Secure Use of Hadoop Web Interface by Big Data Teams. It is estimated that 8000-10000 hadoop installations are at risk across the world including hadoop deployments in the cloud. Increased number of organizations are moving to hadoop in the cloud because of its utter complexity to make hadoop work.

Hadoop 40

Hadoop Food Data Lake Banking 40

ProjectPro

MAY 23, 2015

Big Data Analytics Solutions at Walmart Social Media Big Data Solutions Mobile Big Data Analytics Solutions Walmart’ Carts – Engaging Consumers in the Produce Department World's Biggest Private Cloud at Walmart- Data Cafe How Walmart is fighting the battle against big data skills crisis?

Big Data 52

Big Data Data Analysis Hadoop Retail 52

ProjectPro

FEBRUARY 16, 2023

Businesses will be better able to make smart decisions and achieve a competitive advantage if they can successfully integrate data from various sources using SQL. If your database is cloud-based, using SQL to clean data is far more effective than scripting languages. But how does SQL play a vital role here?

Data Engineer 52

Data Engineer Data Engineering SQL Engineering 52

ProjectPro

JANUARY 31, 2023

Map tasks deal with mapping and data splitting, whereas Reduce tasks shuffle and reduce data. Hadoop can execute MapReduce applications in various languages, including Java, Ruby, Python, and C++. When to use MapReduce with Big Data. The prepared data is then stored in a data warehouse or a similar repository.

Big Data 52

Big Data Hadoop Relational Database AWS 52

ProjectPro

MAY 31, 2021

A big data project is a data analysis project that uses machine learning algorithms and different data analytics techniques on a large dataset for several purposes, including predictive modeling and other advanced analytics applications.

Big Data 52

Big Data Coding Project Hadoop 52

Rockset

APRIL 15, 2022

So why are their analytics still crawling through in batches instead of real time? It’s probably because their analytics database lacks the features necessary to deliver data-driven decisions accurately in real time. All updates are appended rather than written over existing data records.

Analytics Application 52

Analytics Application Data Warehouse Kafka Raw Data 52

phData: Data Engineering

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52

Cloudera

NOVEMBER 22, 2021

In legacy analytical systems such as enterprise data warehouses, the scalability challenges of a system were primarily associated with computational scalability, i.e., the ability of a data platform to handle larger volumes of data in an agile and cost-efficient way. public, private, hybrid cloud)?

Government 92

Government Hadoop Data Security Cloud 92

Edureka

JULY 3, 2024

The Basics How Azure Data Factory Works: Quick Summary Top Features of Azure Data Factory Key Components of Azure Data Factory Azure Data Factory Data Migration: Overview Azure Data Factory: Top Use Cases FAQs Conclusion What is Azure Data Factory?

Pipeline-centric 52

Pipeline-centric Data Lake Database-centric Data Pipeline 52

Rockset

DECEMBER 9, 2021

Companies that undertook big data projects ran head-long into the high cost, rigidity and complexity of managing complex on-premises data stacks. Lifting-and-shifting their big data environment into the cloud only made things more complex. Every layer in the modern data stack was built for a batch-based world.

Transportation 52

Transportation BI SQL Database 52

Cloudera

JUNE 16, 2022

Building real-time data analytics pipelines is a complex problem, and we saw customers struggle using processing frameworks such as Apache Storm, Spark Streaming, and Kafka Streams. . Better yet, it works in any cloud environment. Getting started today.

Kafka 85

Kafka Manufacturing Data Lake SQL 85