Capgemini

JANUARY 17, 2023

Here are four resolutions to make your data strategy pay off this year. Reassess your data architecture. Most executives (72 percent) say that data, both fragmented and with poor quality, is likely to be the biggest issue when aspiring to achieve AI goals. Embrace opensource AI and open standards. Interesting read?

Strategy 52

Strategy Technical Review Data Artificial Inteligence 52

Cloudera

MAY 13, 2021

Our Choose the Right Stream Processing Engine for Your Data Needs whitepaper makes those comparisons for you, so you can quickly and confidently determine which engine best meets your key business requirements. When evaluating a stream processing engine, consider its processing abstraction capabilities.

Engineering 101

Engineering Comparison Open Source Scalability 101

Netflix Tech

MARCH 5, 2019

While our engineering teams have and continue to build solutions to lighten this cognitive load (better guardrails, improved tooling, …), data and its derived products are critical elements to understanding, optimizing and abstracting our infrastructure. Give us a holler if you are interested in a thought exchange.

Infrastructure 73

Infrastructure Scalability Cloud Data 73

Altexsoft

MARCH 14, 2023

This development has paved the way for a suite of cloud-native data tools that are user-friendly, scalable, and affordable. Known as the Modern Data Stack (MDS) , this suite of tools and technologies has transformed how businesses approach data management and analysis. What is a modern data stack? Cloud-first.

Data 59

Data Technical Review Software Review Artificial Inteligence 59

Confluent

FEBRUARY 6, 2019

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way. It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way.

Machine Learning 107

Machine Learning Artificial Inteligence Scalability Data Engineering 107

Altexsoft

OCTOBER 21, 2022

Apache Kafka is an open-source, distributed streaming platform for messaging, storing, processing, and integrating large data volumes in real time. It offers high throughput, low latency, and scalability that meets the requirements of Big Data. Plus the name sounded cool for an open-source project.”.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Cloudera

JUNE 7, 2022

In our very own Enterprise Data Maturity research surveying over 3,000 IT and senior business leaders, we found that 40% of organizations are currently running hybrid but mostly on-premises, and 36% of respondents expect to shift to hybrid multi-cloud in the next 18 months. Where data flows, ideas follow.

Data 108

Data Architecture Analytics Government 108

Altexsoft

JULY 18, 2023

These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. Maintained by the Apache Software Foundation, Apache Spark is an open-source, unified engine designed for large-scale data analytics.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52

Netflix Tech

OCTOBER 18, 2022

As Big data and ML became more prevalent and impactful, the scalability, reliability, and usability of the orchestrating ecosystem have increasingly become more important for our data scientists and the company. Meson was based on a single leader architecture with high availability.

Data 81

Data UI/UX Systems Review Software Review 81

Cloudera

JANUARY 19, 2022

We have been working hard to build our cloud-native data services on Cloudera Data Platform (CDP), which include CDP Data Warehouse, CDP Operational Database, CDP Machine Learning, CDP Data Engineering and CDP Data Flow. Download the reports to see the detailed scores .

Report 87

Report Technical Advisors Cloud Analytics 87

Altexsoft

JULY 29, 2022

Apache Hadoop is an open-source Java-based framework that relies on parallel processing and distributed storage for analyzing massive datasets. Developed in 2006 by Doug Cutting and Mike Cafarella to run the web crawler Apache Nutch, it has become a standard for Big Data analytics. Source: Allied Market Research.

Big Data 59

Big Data Data Google Cloud Storage 59

Cloudera

SEPTEMBER 21, 2021

Many customers looking at modernizing their pipeline orchestration have turned to Apache Airflow, a flexible and scalable workflow manager for data engineers. Take a test drive of Airflow in Cloudera Data Engineering yourself today to learn about its benefits and how it could help you streamline complex data workflows.

Off-The-Shelf 95

Off-The-Shelf Data Engineering Virtualization Cloud 95

Kentik

MARCH 7, 2023

So far in this series , I’ve outlined how a scaling enterprise’s accumulation of data (data gravity) struggles against three consistent forces: cost, performance, and reliability. As datasets scale and networks become distributed to free their data, the data gravity story begins to morph into a data complexity story.

Network 99

Network Cloud Data Case Study 99

Altexsoft

NOVEMBER 7, 2022

You can hardly compare data engineering toil with something as easy as breathing or as fast as the wind. The platform went live in 2015 at Airbnb, the biggest home-sharing and vacation rental site, as an orchestrator for increasingly complex data pipelines. How data engineering works. Source: Apache Airflow.

Weak Development Team 52

Weak Development Team Technical Review Software Review Systems Review 52

Altexsoft

MARCH 18, 2021

The approach is possible thanks to the modern technologies that allow for storing and processing huge volumes of data in any format. This includes Apache Hadoop , an open-source software that was initially created to continuously ingest data from different sources, no matter its type.

Systems Review 109

Systems Review Technical Review Software Review Compliance 109

Altexsoft

OCTOBER 8, 2021

Usually, data integration software is divided into on-premise, cloud-based, and open-source types. On-premise data integration tools. As the name suggests, these tools aim at integrating data from different on-premise source systems. Open-source data integration tools. Source: Snaplogic.

Tools 52

Tools Data Software Review Open Source 52

Altexsoft

APRIL 26, 2022

With the consistent rise in data volume, variety, and velocity, organizations started seeking special solutions to store and process the information tsunami. This demand gave birth to cloud data warehouses that offer flexibility, scalability, and high performance. Data warehousing in a nutshell.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Altexsoft

MAY 14, 2021

On top of that, new technologies are constantly being developed to store and process Big Data allowing data engineers to discover more efficient ways to integrate and use that data. You may also want to watch our video about data engineering: A short video explaining how data engineering works.

Big Data 78

Big Data Analytics Tools Applications 78

KitelyTech

FEBRUARY 3, 2021

It often includes three main components like the database schema, database application, and data engine. The consistent operation between those three components will facilitate data integrity, reliability, consistency, and transaction maintainability. Therefore, the software must have an easily extendable architecture.

Big Data 52

Big Data Data Development Business Intelligence 52

Altexsoft

DECEMBER 14, 2022

Docker is an open-source containerization software platform: It is used to create, deploy and manage applications in virtualized containers. Launched in 2013 as an open-source project, the Docker technology made use of existing computing concepts around containers, specifically the Linux kernel with its features.

Weak Development Team 52

Weak Development Team Linux Operating System Virtualization 52

AWS Machine Learning - AI

JANUARY 26, 2024

Understanding and addressing LLM vulnerabilities, threats, and risks during the design and architecture phases helps teams focus on maximizing the economic and productivity benefits generative AI can bring. Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications.

Artificial Inteligence 119

Artificial Inteligence Generative AI Security Applications 119

Abhishek Tiwari

JULY 12, 2018

Unfortunately, building data pipelines remains a daunting, time-consuming, and costly activity. Not everyone is operating at Netflix or Spotify scale data engineering function. Often companies underestimate the necessary effort and cost involved to build and maintain data pipelines.

Data 63

Data Software Review Technical Review Microservices 63

Altexsoft

OCTOBER 2, 2020

Key disciplines and roles in data management. Data architecture: aligning technologies with business goals. Specialist responsible for the area: data architect. Data architecture is a starting point for any data management model. Snowflake provides computing resources scalable for different workloads.

Database Administration 64

Database Administration Strategy Data Technical Review 64

Hu's Place - HitachiVantara

MARCH 18, 2019

They used a time series data base (TSDB) which is optimized for measuring change over time and makes time series data very different than other data workloads. Open-source monitoring solutions such as openTSDB lack analytics and smart alerting. This improved lead time from 2 days to less than 10 minutes.

Healthcare 61

Healthcare Tourism Systems Review Energy 61

Cloudera

DECEMBER 10, 2020

Drawing on more than a decade of experience in building and deploying massive scale data platforms on economical budgets, Cloudera has designed and delivered a cost-cutting cloud-native solution – Cloudera Data Warehouse (CDW), part of the new Cloudera Data Platform (CDP). Watch this video to get an overview of CDW. .

Data 98

Data Technical Review Storage Systems Review 98

O'Reilly Media - Ideas

MARCH 1, 2023

Content about software development was the most widely used (31% of all usage in 2022), which includes software architecture and programming languages. Software development is followed by IT operations (18%), which includes cloud, and by data (17%), which includes machine learning and artificial intelligence. growth over 2021.

Trends 136

Trends Technical Review Technology Software Review 136

Altexsoft

JUNE 7, 2021

Apache Hadoop is an open-source framework written in Java for distributed storage and processing of huge datasets. The keyword here is distributed since the data quantities in question are too large to be accommodated and analyzed by a single computer. scalability. Hadoop architecture, or how the framework works.

Big Data 98

Big Data Tools Data Storage 98

DataRobot

MARCH 10, 2022

With Snowflake’s newest feature release, Snowpark , developers can now quickly build and scale data-driven pipelines and applications in their programming language of choice, taking full advantage of Snowflake’s highly performant and scalable processing engine that accelerates the traditional data engineering and machine learning life cycles.

Artificial Inteligence 52

Artificial Inteligence Artificial Intelligence Machine Learning IoT 52

Cloudera

SEPTEMBER 9, 2022

The promise of a modern data lakehouse architecture. Imagine having self-service access to all business data, anywhere it may be, and being able to explore it all at once. Imagine quickly answering burning business questions nearly instantly, without waiting for data to be found, shared, and ingested.

Architecture 97

Architecture Innovation Data Open Source 97

Altexsoft

MARCH 16, 2021

In general, a data infrastructure is a system of hardware and software tools used to collect, store, transfer, prepare, analyze, and visualize data. Check our article on data engineering to get a detailed understanding of the data pipeline and its components. Big data infrastructure in a nutshell.

Big Data 116

Big Data Healthcare Applications Data 116

Abhishek Tiwari

DECEMBER 27, 2017

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. Native frameworks.

Big Data 63

Big Data Data Storage Microservices 63

Kentik

JUNE 30, 2015

Our first service, Kentik Detect, is an infrastructure data analytics service that is scalable, powerful, flexible, open, and easy to use. The problem wasn’t getting the data — most switches and routers can generate decent traffic flow via sFlow, NetFlow, or IPFIX. Long-term retention and availability at high resolution.

Load Balancer 40

Load Balancer Analytics Scalability Network 40

Cloudera

MARCH 23, 2022

In fact, we recently announced the integration with our cloud ecosystem bringing the benefits of Iceberg to enterprises as they make their journey to the public cloud, and as they adopt more converged architectures like the Lakehouse. Iceberg is designed to be open and engine agnostic allowing datasets to be shared.

Data 88

Data Open Source Storage Machine Learning 88

Kentik

MAY 31, 2017

It’s high time to move away from this legacy paradigm to a unified, scalable, real-time solution built on the power of big data. Kentik’s founders, who ran large network operations at Akamai, Netflix, YouTube, and Cloudflare, well understand the challenges faced by teams working with siloed legacy tools and fragmented data sets.

Network 40

Network Tools Big Data Metrics 40

CIO

JULY 29, 2022

For example, if a data team member wants to increase their skills or move to a data engineer position, they can embark on a curriculum for up to two years to gain the right skills and experience. The bootcamp broadened my understanding of key concepts in data engineering.

Groups 207

Groups Security Development Data Engineering 207

Cloudera

APRIL 26, 2023

Can you achieve similar outcomes with your on-premises data platform? Application modernization initiatives have led to cloud native architectures gaining popularity on premises, making it a sensible choice to extend to your data platform. This is exactly where cloud native architectures excel, and why they are so popular.

Cloud 63

Cloud Data Load Balancer Storage 63

Cloudera

FEBRUARY 22, 2022

Over the past decade, the successful deployment of large scale data platforms at our customers has acted as a big data flywheel driving demand to bring in even more data, apply more sophisticated analytics, and on-board many new data practitioners from business analysts to data scientists.

Data 105

Data Analytics Disaster Recovery Travel 105