Xebia

MAY 16, 2023

Data Engineers were tempted by the pressure of the moment to give up on testing all together. There was no need for generating your own data; just take a percentage of production data. In many cases, these tasks ended up on the shoulders of the Data Engineers themselves. Overly restrictive governance.

Testing 130

Testing How To Data Engineering Engineering 130

CIO

AUGUST 2, 2023

What are some examples of data solutions in each of those buckets? we’re putting sensors across our manufacturing processes, which give us vast sums of data our leaders use to rethink those processes. Microscopy and gene sequencing, for example, generate a very large amount of data, which our customers are trying to analyze.

Data 230

Data Architecture Government Strategy 230

TechCrunch

MAY 18, 2022

When asked, Heartex says that it doesn’t collect any customer data and open sources the core of its labeling platform for inspection. “We’ve built a data architecture that keeps data private on the customer’s storage, separating the data plane and control plane,” Malyuk added.

Open Source 203

Open Source Weak Development Team Artificial Inteligence Data 203

Cloudera

FEBRUARY 12, 2024

Get it wrong and your deployment may become wholly unusable with users unable to access and use the Cloudera data services. If the DNS is set up less ideal than it could be, connectivity and performance issues may arise. In a Cloudera deployment scenario, only storage accounts, PostgreSQL DB, and Key Vault support service endpoints.

Azure 52

Azure Firewall Data Engineering Storage 52

TechCrunch

APRIL 12, 2022

CEO Ketan Umare says that the proceeds will be put toward supporting the Flyte community by “improving the accessibility, performance and reliability of Flyte” and broadening the array of systems that Flyte integrates with.

Artificial Inteligence 202

Artificial Inteligence Machine Learning Open Source Biotech 202

Altexsoft

FEBRUARY 11, 2023

Data architecture is the organization and design of how data is collected, transformed, integrated, stored, and used by a company. What is the main difference between a data architect and a data engineer? By the way, we have a video dedicated to the data engineering working principles.

Data 87

Data Data Engineering Big Data Architecture 87

Mobilunity

MAY 8, 2023

Key zones of an Enterprise Data Lake Architecture typically include ingestion zone, storage zone, processing zone, analytics zone, and governance zone. Ingestion zone is where data is collected from various sources and ingested into the data lake. Storage zone is where the raw data is stored in its original format.

Engineering 52

Engineering Data Storage AWS 52

Kentik

APRIL 12, 2023

With modern networks’ increasing complexity and scale, it has become essential to collect and analyze data from various sources to gain insights into network performance, security, and availability. DataOps team roles In a DataOps team, several key roles work together to ensure the data pipeline is efficient, reliable, and scalable.

Network 52

Network Data Engineering Machine Learning Artificial Inteligence 52

Altexsoft

AUGUST 29, 2023

In 2010, a transformative concept took root in the realm of data storage and analytics — a data lake. The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data.

Architecture 52

Architecture Data Storage Machine Learning 52

Cloudera

FEBRUARY 1, 2024

In the fast-paced world of cloud-native products, mastering Day 2 operations is crucial for sustaining the performance and stability of Kubernetes-based platforms, such as CDP Private Cloud Data Services. We’ve included a more detailed example of what Days 0, 1, and 2 involve in the appendix if you’re interested.

Backup 82

Backup Cloud Architecture Resources 82

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). 150 for storage use = $15 / TB / month x 10 TB.

Data 98

Data Technical Review Storage Systems Review 98

Cloudera

FEBRUARY 16, 2022

While it is a little dated, one amusing example that has been the source of countless internet memes is the famous, “is this a chihuahua or a muffin?” In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Each user associated with a project performs work via a session.

Machine Learning 83

Machine Learning Artificial Inteligence Weak Development Team Construction 83

Mentormate

OCTOBER 31, 2023

In our example, obvious stakeholders include healthcare providers, patients, and insurers. These are parties indirectly affected by the project, such as local communities, adjacent industries, regulatory bodies, or, in the example of healthcare, even medical researchers. Could more data enhance performance?

Artificial Inteligence 98

Artificial Inteligence Healthcare Budget Training 98

Confluent

FEBRUARY 6, 2019

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. Data scientists love Python, period.

Machine Learning 106

Machine Learning Artificial Inteligence Scalability Data Engineering 106

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. A provider could be used to make HTTP requests, connect to a RDBMS, check file systems (such as S3 object storage), invoke cloud provider services, and much more.

Off-The-Shelf 96

Off-The-Shelf Data Engineering Virtualization Cloud 96

AWS Machine Learning - AI

JANUARY 26, 2024

Lastly, we connect these together with an example LLM workload to describe an approach towards architecting with defense-in-depth security across trust boundaries. We then discuss how building on a secure foundation is essential for generative AI.

Artificial Inteligence 117

Artificial Inteligence Generative AI Security Applications 117

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. Performance.

Big Data 63

Big Data Data Storage Microservices 63

Cloudera

JUNE 18, 2022

As use cases matured, we saw the need for both efficient, interactive BI analytics and transactional semantics to modify data. The first generation of the Hive Metastore attempted to address the performance considerations to run SQL efficiently on a data lake. More formats, more engines, more interoperability.

Data 91

Data Analytics Open Source Architecture 91

Altexsoft

OCTOBER 30, 2021

If you’re an executive who has a hard time understanding the underlying processes of data science and get confused with terminology, keep reading. We will try to answer your questions and explain how two critical data jobs are different and where they overlap. Data science vs data engineering. Feature engineering.

Data Engineering 98

Data Engineering Engineering Data Machine Learning 98

Cloudera

JULY 24, 2023

That first step requires integrating the latest versions of all required open source projects, including not just data processing engines (e.g., Apache Impala, Apache Spark) but also all foundational services needed for storage (e.g., data engineering pipelines, machine learning models).

Open Source 66

Open Source Analytics Software Review Metrics 66

Altexsoft

SEPTEMBER 9, 2022

Another example is labeling and categorizing objects in any scientific field, be it archeological finds, chemical samples, or living things in biology. Metadata is basically information that describes other data. It helps us understand the origin, structure, nature, and context of data. Metadata examples. date modified.

Tools 59

Tools Technical Review Software Review Systems Review 59

Cloudera

SEPTEMBER 1, 2020

Solr is a standard and open source, commonly adopted text search engine with rich query APIs for performing analytics over text and other unstructured data. YARN allows you to use various data processing engines for batch, interactive, and real-time stream processing of data stored in HDFS or cloud storage like S3 and ADLS.

Data 67

Data Backup Disaster Recovery Storage 67

Cloudera

JANUARY 20, 2021

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

Data 106

Data Artificial Inteligence Analytics Machine Learning 106

O'Reilly Media - Ideas

APRIL 9, 2019

A few months ago, I wrote about the differences between data engineers and data scientists. An interesting thing happened: the data scientists started pushing back, arguing that they are, in fact, as skilled as data engineers at data engineering. Data engineering is not in the limelight.

Data Engineering 111

Data Engineering Engineering Data Technical Review 111

Altexsoft

APRIL 13, 2023

to see where freight is in real time and what the ETA is, share this information with customers, monitor and analyze fleet/carrier performance, and much more. Previously, control towers were seen as multi-faceted dashboards to monitor performance. Sometimes, they were designed to control a particular aspect of a supply chain.

Technical Review 59

Technical Review Software Review Analytics Systems Review 59

Altexsoft

OCTOBER 29, 2020

But before you dive in, we recommend you reviewing our more beginner-friendly articles on data transformation: Complete Guide to Business Intelligence and Analytics: Strategy, Steps, Processes, and Tools. What is Data Engineering: Explaining the Data Pipeline, Data Warehouse, and Data Engineer Role.

Testing 63

Testing Tools Software Review Technical Review 63

Cloudera

DECEMBER 20, 2022

This is due to an improvement in plant uptime, supported by predictive maintenance applications, coupled with optimization of the fuel consumption that powers these plants, as well as fine-grained performance monitoring that eliminates over-production. . These include recruitment, training, performance management, and employee retention.

Energy 81

Energy Analytics Data Machine Learning 81

Netflix Tech

DECEMBER 21, 2020

By Anupom Syam Background At Netflix, our current data warehouse contains hundreds of Petabytes of data stored in AWS S3 , and each day we ingest and create additional Petabytes. 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

Altexsoft

APRIL 22, 2020

So, along with data scientists who create algorithms, there are data engineers, the architects of data platforms. In this article we’ll explain what a data engineer is, the field of their responsibilities, skill sets, and general role description. What is a data engineer?

Data Engineering 69

Data Engineering Engineering Artificial Inteligence Data 69

KitelyTech

FEBRUARY 3, 2021

Database management is what your database administrator uses to store, organize, and access computer data. It often includes three main components like the database schema, database application, and data engine. Database management systems must be flexible enough to handle large amounts of data in a short period of time.

Big Data 52

Big Data Data Development Business Intelligence 52

Existek

JANUARY 11, 2022

In 2010, they launched Windows Azure, the PaaS, positioning it as an alternative to Google App Engine and Amazon EC2. They provided a few services like computing, Azure Bob storage, SQL Azure, and Azure Service Bus. For example, Amazon started to divide separate data units for goods, orders, and customers. Business apps.

Azure 52

Azure AWS Cloud How To 52

Altexsoft

AUGUST 25, 2020

That’s why a data specialist with big data skills is one of the most sought-after IT candidates. Data Engineering positions have grown by half and they typically require big data skills. Data engineering vs big data engineering. This greatly increases data processing capabilities.

Big Data 75

Big Data Data Engineering Engineering Data 75

TIBCO - Connected Intelligence

APRIL 22, 2021

In its annual Worldwide Global Datasphere Forecast, 2019-2023, IDC projected that only 15% of annual data growth is actually net new data. That means 85% of data growth results from copying data you already have. Business-friendly data views simplify access and hide IT complexity.

Sustainability 46

Sustainability Virtualization Data Energy 46

Abhishek Tiwari

DECEMBER 22, 2017

Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. Then we perform frequent batch ETL from application databases to a data warehouse. Classic ETL. Stateless and elastic.

Storage 40

Storage Big Data Google Cloud Analysis 40

Cloudera

FEBRUARY 14, 2024

It offers several benefits such as schema evolution, hidden partitioning, time travel, and more that improve the productivity of data engineers and data analysts. However, you need to regularly maintain Iceberg tables to keep them in a healthy state so that read queries can perform faster.

Strategy 57

Strategy Travel Groups Data 57

Netflix Tech

NOVEMBER 22, 2019

December 2 1pm-2pm CMP 326-R Capacity Management Made Easy with Amazon EC2 Auto Scaling Vadim Filanovsky , Senior Performance Engineer & Anoop Kapoor, AWS Abstract :Amazon EC2 Auto Scaling offers a hands-free capacity management experience to help customers maintain a healthy fleet, improve application availability, and reduce costs.

AWS 40

AWS Open Source Linux Off-The-Shelf 40

Netflix Tech

NOVEMBER 22, 2019

December 2 1pm-2pm CMP 326-R Capacity Management Made Easy with Amazon EC2 Auto Scaling Vadim Filanovsky , Senior Performance Engineer & Anoop Kapoor, AWS Abstract :Amazon EC2 Auto Scaling offers a hands-free capacity management experience to help customers maintain a healthy fleet, improve application availability, and reduce costs.

AWS 40

AWS Open Source Linux Off-The-Shelf 40