Cloudera

SEPTEMBER 15, 2022

Modern data platforms deliver an elastic, flexible, and cost-effective environment for analytic applications by leveraging a hybrid, multi-cloud architecture to support data fabric, data mesh, data lakehouse and, most recently, data observability. The post Demystifying Modern Data Platforms appeared first on Cloudera Blog.

Data Lake 83

Data Lake Analytics Application Cloud Storage Architecture 83

Christophe Blefari

JANUARY 7, 2023

The blog crossed the 2000 members mark (❤️) and I won the best data science newsletter award. Introducing ADBC: Database Access for Apache Arrow — When I see "minimal-overhead alternative to JDBC/ODBC for analytical applications" I'm instantly in. I think this is even relevant to data world.

Data Science 130

Data Science Data BI Kafka 130

Cloudera

SEPTEMBER 1, 2020

It is designed to simplify deployment, configuration, and serviceability of Solr-based analytics applications. DDE also makes it much easier for application developers or data workers to self-service and get started with building insight applications or exploration services based on text or other unstructured data (i.e.

Cloud Storage 71

Cloud Storage Unstructured Data AWS Analytics Application 71

Cloudera

MAY 30, 2024

This unified data environment eliminates the need for maintaining separate data silos and facilitates seamless access to data for AI and analytics applications. The post Unify your data: AI and Analytics in an Open Lakehouse appeared first on Cloudera Blog. Learn more about the Cloudera Open Data Lakehouse here.

Data Lake 74

Data Lake Data Warehouse Programming Language Data Ingestion 74

Striim

MARCH 21, 2025

You end up having data flowing in the right format for your cloud analytics solution. The main value from this is that you can now run operational workloads, high value or high operational value producing analytics applications in your analytics solution.

Cloud 52

Cloud Database Architecture BI 52

Striim

MARCH 21, 2025

You end up having data flowing in the right format for your cloud analytics solution. The main value from this is that you can now run operational workloads, high value or high operational value producing analytics applications in your analytics solution.

Cloud 52

Cloud Database Architecture BI 52

Cloudera

SEPTEMBER 15, 2022

In this blog post, we will talk about a single Ozone cluster with the capabilities of both Hadoop Core File System (HCFS) and Object Store (like Amazon S3). Please refer to our earlier Cloudera blog for more details about Ozone’s performance benefits and atomicity guarantees. The same data can be read as an object, or a file.

Systems 87

Systems Hadoop Metadata Telecommunication 87

Workfall

JUNE 27, 2023

In this blog, we’ll explore how to harness the power of Kafka to streamline event streaming within a microservices architecture and unlock its full potential for building scalable and responsive systems. Conclusion In this blog, we demonstrated how we can introduce Kafka as a message broker into a microservices architecture.

Kafka 75

Kafka Architecture AWS Transportation 75

Cloudera

FEBRUARY 8, 2024

Overview This blog post describes support for materialized views for the Iceberg table format. Apache Iceberg is a high-performance open table format for petabyte-scale analytic datasets. In a future blog, we will evaluate the incremental versus full rebuild performance. These tables are created as Iceberg tables.

Metadata 91

Metadata Data Warehouse BI AWS 91

Cloudera

DECEMBER 1, 2022

For analytic applications to properly leverage a hybrid, multi-cloud ecosystem to support modern data architectures, data observability has become even more important. The post You Can’t Hit What You Can’t See appeared first on Cloudera Blog. Source: IDC .

Data Lake 84

Data Lake Data Pipeline Analytics Application Data Governance 84

Cloudera

NOVEMBER 2, 2020

In the meantime, if you want to learn more, please check out this video, which shows how to build an end-to-end Event Analytics application in CDP, using Apache Kafka, Apache Druid, Apache Hive, and Cloudera DataViz. The post An Overview of Real Time Data Warehousing on Cloudera appeared first on Cloudera Blog.

Data Warehouse 97

Data Warehouse Kafka Lambda Architecture Telecommunication 97

Cloudera

JUNE 16, 2022

This blog aims to answer two questions as illustrated in the diagram below: How have stream processing requirements and use cases evolved as more organizations shift to “streaming first” architectures and attempt to build streaming analytics pipelines? The post Turning Streams Into Data Products appeared first on Cloudera Blog.

Kafka 88

Kafka Manufacturing Data Lake SQL 88

Cloudera

NOVEMBER 8, 2022

It enables cloud-native applications to store and process mass amounts of data in a hybrid multi-cloud environment and on premises. These could be traditional analytics applications like Spark, Impala, or Hive, or custom applications that access a cloud object store natively. Conclusion.

Metadata 84

Metadata Algorithm Hadoop Cloud 84

Rockset

FEBRUARY 18, 2021

times faster than Druid in the latest performance blog post. Real-time analytics is all about deriving insights and taking actions as soon as data is produced. When broken down into its core requirements, real-time analytics means two things: access to fresh data and fast responses to queries. Learn how Rockset is 1.67

Analytics Application 52

Analytics Application SQL Database Structured Data 52

Data Science Blog: Data Engineering

JULY 4, 2022

For fast analytic queries against another size of data, it uses in-memory caching and optimised query execution. It is a parallel processing framework for grouped computers to operate large-scale data analytics applications. The post 5 Apache Spark Best Practices appeared first on Data Science Blog (English only).

Hadoop 52

Hadoop Big Data Datasets Scala 52

Cloudera

NOVEMBER 22, 2021

The post Addressing the Three Scalability Challenges in Modern Data Platforms appeared first on Cloudera Blog. benchmarking study conducted by independent 3rd party ).

Hadoop 94

Hadoop Government Data Security Cloud 94

Data Science Blog: Data Engineering

NOVEMBER 15, 2023

An object-centric data model is a big deal because it offers the opportunity for a holistic approach and as a database a single source of truth for Process Mining but also for other types of analytical applications. The post Object-centric Process Mining on Data Mesh Architectures appeared first on Data Science Blog (English only).

Architecture 52

Architecture Database-centric Process BI 52

Rockset

JANUARY 30, 2024

In this blog, we delve into each of these features and how they are giving users more cost controls for their search and AI applications. Users may be building user-facing search and analytics applications on data that is updated after minutes or hours.

Data Ingestion 64

Data Ingestion Utilities Architecture SQL 64

Rockset

MARCH 10, 2022

We'll be publishing more posts in the series in the near future, so subscribe to our blog so you don't miss them! If you want to see all of the key requirements of real-time analytics databases, watch my recent talk at the Hive on Designing the Next Generation of Data Systems for Real-Time Analytics , available below.

Data Analytics 52

Data Analytics Data Warehouse MySQL Medical 52

Rockset

MARCH 15, 2021

The Demands of Real-Time Analytics Real-time analytics applications have specific demands (i.e., and your solution will only be able to provide valuable real-time analytics if you are able to meet them. Indexing Efficiency Indexing data is another crucial requirement for real-time analytics applications.

MongoDB 40

MongoDB Data Ingestion Analytics Application Kafka 40

Rockset

MAY 17, 2022

We'll be publishing more posts in the series in the near future, so subscribe to our blog so you don't miss them!

SQL 52

SQL NoSQL Hadoop MongoDB 52

Cloudera

MARCH 6, 2018

This leads to extra cost, effort, and risk to stitch together a sub-optimal platform for multi-disciplinary, cloud-based analytics applications. The post Altus SDX: Shared services for cloud-based analytics appeared first on Cloudera Blog. Further, much of the value of cloud is for elastic workloads.

Cloud 40

Cloud Metadata Big Data Analytics Application 40

Snowflake

MAY 4, 2023

The way we’d normally design analytical applications is to build something outside of our data environment, manage the infrastructure and compute, connect to Snowflake for the data, take the data back to the compute platform, and give an interface to our users. Click here for all the details.

Portfolio 52

Portfolio Cloud Analytics Application Data Security 52

Rockset

OCTOBER 26, 2023

That’s why JetBlue innovates with real-time analytics and AI, using over 15 machine learning applications in production today for dynamic pricing, customer personalization, alerting applications, chatbots and more.

Machine Learning 64

Machine Learning Data Science Architecture Database 64

Cloudera

NOVEMBER 13, 2017

Cloud PaaS takes this a step further and allows users to focus directly on building data pipelines, training machine learning models, developing analytics applications — all the value creation efforts, vs the infrastructure operations. The post The Future of Cloud-based Analytics (Part 3) appeared first on Cloudera Blog.

Cloud 40

Cloud Big Data Metadata Machine Learning 40

Cloudera

DECEMBER 7, 2017

Now we are releasing the reference architecture for you build your own self-managed SDX foundation for all your cloud-based data and analytics applications. Cloud data that can efficiently drive all your machine learning and analytics initiatives. It’s time for a new approach to analytics in the cloud. It’s time for SDX.

Building 40

Building Management Government BI 40

Rockset

JULY 6, 2022

We'll be publishing more posts in the series in the near future, so subscribe to our blog so you don't miss them!

NoSQL 52

NoSQL SQL Systems PostgreSQL 52

Cloudera

FEBRUARY 9, 2021

A typical approach that we have seen in customers’ environments is that ETL applications pull data with a frequency of minutes and land it into HDFS storage as an extra Hive table partition file. In this way, the analytic applications are able to turn the latest data into instant business insights.

Data Warehouse 72

Data Warehouse Cloud Kafka Cloud Storage 72

Rockset

FEBRUARY 24, 2023

Introduction Let’s get this out of the way at the beginning: understanding effective streaming data architectures is hard, and understanding how to make use of streaming data for analytics is really hard. There’s enough content here for its own blog post, but we’ll cover the high-level differences briefly. Kafka or Kinesis ?

Kafka 52

Kafka AWS Amazon Web Services Programming Language 52

Cloudera

APRIL 2, 2019

Apache HBase® is one of many analytics applications that benefit from the capabilities of Intel Optane DC persistent memory. HBase is a distributed, scalable NoSQL database that enterprises use to power applications that need random, real time read/write access to semi-structured data.

NoSQL 48

NoSQL Google Cloud Hadoop Machine Learning 48

Rockset

JANUARY 23, 2024

When building applications on change data capture (CDC) data using Elasticsearch, you’ll want to architect the system to handle frequent updates or modifications to the existing documents in an index. In this blog, we’ll walk through the different options available for updates including full updates, partial updates and scripted updates.

Metadata 52

Metadata Coding Analytics Application Python 52

Rockset

MAY 3, 2023

In this blog, we’ll walk through the benchmark framework, configuration and results. We’ll also delve under the hood of the two databases to better understand why their performance differs when it comes to search and analytics on high-velocity data streams. Rockset achieved up to 4x higher throughput and 2.5x

Data Ingestion 59

Data Ingestion Kafka Database Architecture 59

Cloudera

SEPTEMBER 10, 2018

Using a team of just three administrators (rather than the 15 they needed before) and thanks to SDX providing single consistent context, they can now deploy their Cloudera analytics applications to no fewer than three public clouds in less than an hour. With that in place, what will be your first project?

Data Warehouse 40

Data Warehouse Metadata Cloud Retail 40

Rockset

DECEMBER 9, 2019

It continuously ingests raw data from multiple sources--data lakes, data streams, databases--into its storage layer and allows fast SQL access from both visualisation tools and analytic applications.

Data Engineering 40

Data Engineering Data Engineer Engineering Raw Data 40

AltexSoft

OCTOBER 21, 2022

Hadoop fits heavy, not time-critical analytics applications that generate insights for long-term planning and strategic decisions. If you are interested in web development, take a look at our blog post on. Hadoop works with batches while Kafka deals with streams, but all the same, the similarities are obvious. Kafka vs ETL.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

Edureka

AUGUST 23, 2024

The AWS training will prepare you to become a master of the cloud, storing, processing, and developing applications for the cloud data. This blog will explore the AWS Amazon Kinesis and how this managed platform can revamp data analytics. Table of Content What is Amazon Kinesis? How Amazon Kinesis Works?

AWS 52

AWS Kafka Amazon Web Services Medical 52

U-Next

SEPTEMBER 21, 2022

And while employing it is a fairly new technology, it already has a wide range of applications. This blog will look at the best contemporary applications of Artificial Intelligence in business. . Applications of AI in Business Operations . Artificial Intelligence in Business .

Insurance 40

Insurance Finance Transportation Machine Learning 40