Altexsoft

MARCH 31, 2020

It means you must collect transactional data and move it from the database that supports transactions to another system that can handle large volumes of data. And, as is common, to transform it before loading to another storage system. But how do you move data? The simplest illustration for a data pipeline.

Data 76

Data Storage Analytics Data Center 76

Cloudera

JULY 27, 2021

When it comes to Data and AI, the industry is increasingly committed to a hybrid cloud approach. Reach out to me personally to learn more or follow my viewpoint through blogs and other channels at cloudera.com/blogs. The post Driving Standards & Collaboration in Telco with Data & AI appeared first on Cloudera Blog.

Telecommunications 61

Telecommunications Data Architecture Big Data 61

Confluent

FEBRUARY 6, 2019

The blog posts How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka and Using Apache Kafka to Drive Cutting-Edge Machine Learning describe the benefits of leveraging the Apache Kafka ® ecosystem as a central, scalable and mission-critical nervous system. For now, we’ll focus on Kafka.

Machine Learning 107

Machine Learning Artificial Inteligence Scalability Data Engineering 107

Cloudera

SEPTEMBER 1, 2020

The CrunchIndexerTool can use Spark to read data from HDFS files into Apache Solr for indexing, and run the data through a so-called morphline for extraction and transformation in an efficient way. For the examples presented in this blog, we assume you have a CDP account already. Before you Get Started. Prerequisites.

Data 67

Data Backup Disaster Recovery Storage 67

Cloudera

JANUARY 20, 2021

The missing chapter is not about point solutions or the maturity journey of use cases, the missing chapter is about the data, it’s always been about the data, and most importantly the journey data weaves from edge to artificial intelligence insight. . STEP 3: Monitor data throughput from each factory.

Data 105

Data Artificial Inteligence Analytics Machine Learning 105

Cloudera

AUGUST 31, 2021

While this “data tsunami” may pose a new set of challenges, it also opens up opportunities for a wide variety of high value business intelligence (BI) and other analytics use cases that most companies are eager to deploy. . Traditional data warehouse vendors may have maturity in data storage, modeling, and high-performance analysis.

Data 80

Data Analytics Cloud Technical Review 80

Netflix Tech

DECEMBER 21, 2020

At this scale, we can gain a significant amount of performance and cost benefits by optimizing the storage layout (records, objects, partitions) as the data lands into our warehouse. We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits.

Storage 81

Storage Data Resources Data Engineering 81

TIBCO - Connected Intelligence

DECEMBER 11, 2020

We have been working with TIBCO for over 18 years, initially focused on enterprise application integration, service-oriented architecture, events, messaging, and other capabilities that optimize our client’s operations. As an example, in 2013 we helped one of our customers move all event-based integrations and analytics to the cloud.

Business Transformation 52

Business Transformation Data Analytics Hotels 52

Altexsoft

FEBRUARY 10, 2021

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

Analytics 75

Analytics DevOps Development Software Review 75

TIBCO - Connected Intelligence

MARCH 5, 2020

To help you answer these questions, in part one of this blog series I contrasted technology predictions (less than sure projections of the future) so prevalent at this time of year, versus sure technology bets (those things you can be 100% sure will happen). Data Is The Wisest Bet for 2020. And how do you raise your odds of success?

Technology 42

Technology Games Trends IoT 42

AWS Machine Learning - AI

JANUARY 26, 2024

Plan for rollback and recovery from production security events and service disruptions such as prompt injection, training data poisoning, model denial of service, and model theft early on, and define the mitigations you will use as you define application requirements.

Artificial Inteligence 119

Artificial Inteligence Generative AI Security Applications 119

O'Reilly Media - Ideas

JANUARY 25, 2024

Use of content about event-driven architecture is relatively small, but it’s up 40%. That change is important because event-driven architecture is a tool for designing large systems that have to ingest data from many different streams in real time. And more than 1,000 people signed up for our Generative AI for Healthcare event.

Trends 118

Trends Technical Review Technology Artificial Inteligence 118

Datavail

MARCH 10, 2022

Data stewards – provide oversight of data sets to maintain data integrity and ensure implementation of policies from the committee and end-user compliance with the policies. Others – data modelers, data engineers, data architects, and data quality analysts also contribute to the DG process.

Government 40

Government Strategy Weak Development Team Company 40

Altexsoft

AUGUST 19, 2022

In our blog, we’ve been talking a lot about the importance of business intelligence (BI), data analytics, and data-driven culture for any company. You get 10GB of cloud storage and can upload 1GB of data at a time. Multiple studies continuously demonstrate the superiority of analytics-based organizations (e.g.,

Weak Development Team 52

Weak Development Team Data Azure Analytics 52

Cloudera

SEPTEMBER 17, 2020

With growing disparate data across everything from edge devices to individual lines of business needing to be consolidated, curated, and delivered for downstream consumption, it’s no wonder that data engineering has become the most in-demand role across businesses — growing at an estimated rate of 50% year over year.

Data Engineering 102

Data Engineering Engineering Data Tools 102

Kentik

NOVEMBER 16, 2015

Here at Kentik, our Kentik Detect service is powered by a multi-tenant big data datastore called Kentik Data Engine. KDE handles — on a daily basis — tens of billions of network flow records, ingestion of several TB of data, and many millions of sub-queries. Workers are processes that run on our storage nodes.

Metrics 40

Metrics Microservices Linux Architecture 40

Cloudera

MAY 13, 2021

Since my last blog, What you need to know to begin your journey to CDP , we received many requests for a tool from Cloudera to analyze the workloads and help upgrade or migrate to Cloudera Data Platform (CDP). We might find the root cause by realizing that a problem recurs at a particular time, or coincides with another event. .

Data Engineering 69

Data Engineering Cloud Weak Development Team Resources 69

Cloudera

SEPTEMBER 14, 2020

Everybody needs more data and more analytics, with so many different and sometimes often conflicting needs. Data engineers need batch resources, while data scientists need to quickly onboard ephemeral users. The post Fundamentals for Success in Cloud Data Management appeared first on Cloudera Blog.

Cloud 108

Cloud Data Compliance Government 108

Cloudera

JULY 13, 2023

A range of Iceberg table analysis such as listing table’s data file, selecting table snapshot, partition filtering, and predicate filtering can be delegated through Iceberg Java API instead, obviating the need for each query engine to implement it themself. However, Iceberg Java API calls are not always cheap.

Weak Development Team 69

Weak Development Team Engineering Analytics Storage 69

Cloudera

NOVEMBER 2, 2020

Having a live view of all aspects of their network lets them identify potentially faulty hardware in real time so they can avoid impact to customer call/data service. Ingest 100s of TB of network event data per day . Updates and deletes to ensure data correctness. Basic Architecture for Real-Time Data Warehousing.

Data 91

Data Analytics Storage Big Data 91

Cloudera

FEBRUARY 8, 2022

In the report, Cloudera scored at or above “Good: meets requirements,” on the four assessed use cases: Traditional Transactions, Augmented Transaction Processing, Stream/Event Processing and Operational Intelligence. . Software engineering can now continue to innovate without impacting data engineering and existing ETL jobs.

System 66

System Cloud Azure AWS 66

Cloudera

APRIL 15, 2021

In this blog we will cover the new features in the 7.1.6 release and the new in-place upgrade from HDP that completely does away with replacing infrastructure and data migrations. Data Engineering . Hive Warehouse Connector (HWC) makes data engineering simpler and faster. Data Warehouse.

Cloud 67

Cloud Data Engineering Resources Engineering 67

Perficient

APRIL 1, 2024

Traditionally, organizations used to provision multiple services of Azure Services, like Azure Storage, Azure Databricks, etc. This data needs to be ingested on a real-time basis into the warehouse, for further usage by the data scientist & data analyst team. Terminologies Used Below are few terms used in the blog.

Data 64

Data Azure Case Study Data Engineering 64

Netflix Tech

FEBRUARY 9, 2022

Let’s define some requirements that we are interested in delivering to the Netflix data engineers or anyone who would like to schedule a workflow with some external assets in it. This causes the user-managed storage system to be a critical runtime dependency. Real example of a deployment script.

Data 80

Data Testing Software Review Systems Review 80

StubHub

AUGUST 8, 2019

They are like our customers: they love concerts, festivals, sporting events, theater?—?basically basically any and all live events they can experience. Thygesen: For my team, I’m working on the event page redesign for some of our more popular international domains. Q: What tech solutions have you employed for your project(s)?

Technical Review 8

Technical Review Software Review Software Engineering Sport 8

Cloudera

OCTOBER 25, 2022

“They combine the best of both worlds: flexibility, cost effectiveness of data lakes and performance, and reliability of data warehouses.”. It allows users to rapidly ingest data and run self-service analytics and machine learning. Make sure you have encryption for data in motion as well as at rest. Data loss prevention.

Cloud 52

Cloud Data Firewall AWS 52

Cloudera

OCTOBER 18, 2022

Much of Cloudera’s internal research and development infrastructure for CDP Public Cloud and CDP Private Cloud runs on compute and storage from the big three cloud providers, and at the beginning of 2020 costs were on course to top $25 million per year. When we can do this, we can put resources where they matter most.

Cloud 85

Cloud Analytics AWS Engineering 85

Altexsoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. This scenario involves three main characters — publishers, subscribers, and a message or event broker. All data goes through the middleman — in our case, Kafka — that manages messages and ensures their security.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Altexsoft

MAY 14, 2021

A growing number of companies now use this data to uncover meaningful insights and improve their decision-making, but they can’t store and process it by the means of traditional data storage and processing units. Key Big Data characteristics. Data storage and processing. Traditional approach.

Big Data 78

Big Data Analytics Tools Applications 78

Cloudera

APRIL 9, 2021

Legacy data warehouse solutions are often inefficient due to their scale-up architecture, attempting to serve multiple phases of the data lifecycle with a single monolithic architecture, ineffective management and performance tuning tools. . ETL jobs and staging of data often often require large amounts of resources.

Data 58

Data Software Review Technical Review Architecture 58

Cloudera

AUGUST 20, 2021

The Cloudera Data Platform comprises a number of ‘data experiences’ each delivering a distinct analytical capability using one or more purposely-built Apache open source projects such as Apache Spark for Data Engineering and Apache HBase for Operational Database workloads.

Strategy 94

Strategy Data Technical Review Weak Development Team 94

StubHub

SEPTEMBER 20, 2018

Data contains an infinite number of fascinating insights just waiting to be discovered. As a company, we have data from 48 countries for over 10 million live sports, music and theater events. To capture, analyze, and leverage all of this data is no small feat. Infinite capacity and scalability The data system?—?including

Data 40

Data Scalability Analytics Sport 40

Cloudera

DECEMBER 12, 2018

For example, Impala uses the Hive metastore catalog as its data dictionary and it operates directly on data existing in HDFS, which is found through the Namenode API. So, they have the same components: a catalog, query compilation, query execution, and file management and storage, yet they’re independent of each other.

Data 41

Data Marketing Storage Big Data 41

Cloudera

APRIL 1, 2024

Cloudera, a leader in big data analytics, provides a unified Data Platform for data management, AI, and analytics. Our customers run some of the world’s most innovative, largest, and most demanding data science, data engineering, analytics, and AI use cases, including PB-size generative AI workloads.

Artificial Inteligence 101

Artificial Inteligence Cloud Generative AI Analytics 101