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

CIO

DECEMBER 22, 2022

DataOps (data operations) is an agile, process-oriented methodology for developing and delivering analytics. It brings together DevOps teams with data engineers and data scientists to provide the tools, processes, and organizational structures to support the data-focused enterprise. What is DataOps?

Analytics 325

Analytics Data Engineering Machine Learning Artificial Inteligence 325

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). 2,300 / month for the cloud hardware costs.

Data 98

Data Technical Review Storage Systems Review 98

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

Cloudera

NOVEMBER 28, 2023

Cloudera Private Cloud Data Services is a comprehensive platform that empowers organizations to deliver trusted enterprise data at scale in order to deliver fast, actionable insights and trusted AI. This means you can expect simpler data management and drastically improved productivity for your business users.

Data 78

Data Scalability Analytics Quality Assurance 78

CIO

JUNE 14, 2023

Going from petabytes (PB) to exabytes (EB) of data is no small feat, requiring significant investments in hardware, software, and human resources. This can be achieved by utilizing dense storage nodes and implementing fault tolerance and resiliency measures for managing such a large amount of data. Focus on scalability.

Data 162

Data Scalability Storage Big Data 162

Existek

JANUARY 11, 2022

It eliminated the need to get back to the traditional environment when teams struggled with complex and costly in-house hardware and software. . At some point, cloud computing has changed how to streamline business processes and deal with data in general. Comparing AWS vs Azure use cases, it embraces similar areas. Read the article.

Azure 52

Azure AWS Cloud How To 52

Cloudera

APRIL 26, 2023

It offers features such as data ingestion, storage, ETL, BI and analytics, observability, and AI model development and deployment. The platform offers advanced capabilities for data warehousing (DW), data engineering (DE), and machine learning (ML), with built-in data protection, security, and governance.

Cloud 63

Cloud Data Load Balancer Storage 63

Altexsoft

JANUARY 22, 2020

As a result, it became possible to provide real-time analytics by processing streamed data. Please note: this topic requires some general understanding of analytics and data engineering, so we suggest you read the following articles if you’re new to the topic: Data engineering overview.

Analytics 20

Analytics Data IoT Analysis 20

Altexsoft

DECEMBER 23, 2022

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Data engineering in 14 minutes. Scalability. Please note!

Tools 52

Tools Software Review Systems Review Testing 52

Kentik

MAY 31, 2023

For many enterprises, applications represent only a portion of a much larger reliability mandate, including offices, robotics, hardware, and IoT, and the complex networking, data, and observability infrastructure required to facilitate such a mandate.

Network 52

Network Cloud Load Balancer Firewall 52

Cloudera

DECEMBER 20, 2023

Those models are trained or augmented with data from a data management platform. The data management platform, models, and end applications are powered by cloud infrastructure and/or specialized hardware.

Artificial Inteligence 70

Artificial Inteligence Artificial Intelligence Enterprise Machine Learning 70

Altexsoft

JANUARY 14, 2021

The variety of data explodes and on-premises options fail to handle it. Apart from the lack of scalability and flexibility offered by modern databases, the traditional ones are costly to implement and maintain. At the moment, cloud-based data warehouse architectures provide the most effective employment of data warehousing resources.

Backup 115

Backup Azure Software Review Systems Review 115

Altexsoft

DECEMBER 4, 2020

Hardware and software become obsolete sooner than ever before. So data migration is an unavoidable challenge each company faces once in a while. Transferring data from one computer environment to another is a time-consuming, multi-step process involving such activities as planning, data profiling, testing, to name a few.

Software Review 69

Software Review Software Data Technical Review 69

Altexsoft

JULY 29, 2022

What happens, when a data scientist, BI developer , or data engineer feeds a huge file to Hadoop? Under the hood, the framework divides a chunk of Big Data into smaller, digestible parts and allocates them across multiple commodity machines to be processed in parallel. Scalability. Apache Hadoop architecture.

Big Data 59

Big Data Data Google Cloud Storage 59

Altexsoft

JULY 18, 2023

Its flexibility allows it to operate on single-node machines and large clusters, serving as a multi-language platform for executing data engineering , data science , and machine learning tasks. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52

Altexsoft

MARCH 18, 2021

This includes Apache Hadoop , an open-source software that was initially created to continuously ingest data from different sources, no matter its type. Cloud data warehouses such as Snowflake, Redshift, and BigQuery also support ELT, as they separate storage and compute resources and are highly scalable.

Systems Review 109

Systems Review Technical Review Software Review Compliance 109

Altexsoft

DECEMBER 14, 2022

What’s more, this software may run either partly or completely on top of different hardware – from a developer’s computer to a production cloud provider. Thus, the guest operating system can be installed on this virtual hardware, and from there, applications can be installed and run in the same way as in the host operating system.

Weak Development Team 52

Weak Development Team Linux Operating System Virtualization 52

Cloudera

APRIL 22, 2021

Storage plays one of the most important roles in the data platforms strategy, it provides the basis for all compute engines and applications to be built on top of it. Businesses are also looking to move to a scale-out storage model that provides dense storages along with reliability, scalability, and performance.

Data 98

Data Storage Architecture Big Data 98

Altexsoft

JUNE 6, 2019

GPUs provide a huge computational power, so systems that run on such hardware can efficiently analyze vast amounts of data and generate more accurate forecasting results. Having a huge team of customer-facing agents with the ability to interact on the fly is not sustainable or scalable.

Artificial Inteligence 89

Artificial Inteligence Artificial Intelligence Retail Innovation 89

Altexsoft

OCTOBER 21, 2022

It offers high throughput, low latency, and scalability that meets the requirements of Big Data. The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. But for high availability and data loss prevention, it’s recommended that you have at least three brokers.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Software Review 93

Altexsoft

APRIL 26, 2022

Not long ago setting up a data warehouse — a central information repository enabling business intelligence and analytics — meant purchasing expensive, purpose-built hardware appliances and running a local data center. This demand gave birth to cloud data warehouses that offer flexibility, scalability, and high performance.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Altexsoft

FEBRUARY 8, 2021

Depending on the complexity of your data architecture, consider hiring a business analyst , data engineer , or a team of data scientists to manage your company’s data in a most efficient way. Think about the functionality you want and results you’re looking for.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

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

Altexsoft

MARCH 16, 2021

Let’s take a quick look at the big data infrastructure. Big Data infrastructure in healthcare. In general, a data infrastructure is a system of hardware and software tools used to collect, store, transfer, prepare, analyze, and visualize data. Big data infrastructure in a nutshell. Staffing problems.

Big Data 116

Big Data Healthcare Applications Data 116

Altexsoft

OCTOBER 8, 2021

It’s worth remembering though that open-source projects may entail hidden costs related to purchasing hardware, building networks, training staff, etc. Not to mention that they require a decent level of expertise to develop, deploy, and maintain data integration flows. As a rule, good data integration products have.

Tools 52

Tools Data Software Review Open Source 52

Altexsoft

OCTOBER 2, 2020

A data architect focuses on building a robust infrastructure so that data brings business value. Data modeling: creating useful and meaningful data entities. Data integration and interoperability: consolidating data into a single view. Snowflake provides computing resources scalable for different workloads.

Database Administration 64

Database Administration Strategy Data Technical Review 64

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 . By doing so the benefits to ingest speed, query latency, and scalability can be huge. Data Hub – .

Data 90

Data Analytics Storage Big Data 90

Altexsoft

MAY 14, 2021

The concept of Big Data isn’t new: It has been the desired fruit for several decades already as the capabilities of software and hardware have made it possible for companies to successfully manage vast amounts of complex data. Big Data analytics processes and tools. Data ingestion. Apache Kafka.

Big Data 78

Big Data Analytics Tools Applications 78

Altexsoft

JUNE 7, 2021

scalability. Hadoop allows you to leverage data from multiple sources and in different formats, both structured and unstructured. You don’t need to archive or clean data before loading. Hadoop works on low-cost, commodity hardware which makes it relatively cheap to maintain. No real-time data processing.

Big Data 98

Big Data Tools Data Storage 98

Altexsoft

OCTOBER 24, 2019

And this is what makes a data warehouse different from a Data Lake. Data Lakes are used to store unstructured data for analytical purposes. But unlike warehouses, data lakes are used more by data engineers/scientists to work with big sets of raw data. Subject-oriented data.

Architecture 50

Architecture Enterprise Data Technical Review 50

O'Reilly Media - Ideas

MARCH 1, 2023

That’s a fairly good picture of our core audience’s interests: solidly technical, focused on software rather than hardware, but with a significant stake in business topics. The topics that saw the greatest growth were business (30%), design (23%), data (20%), security (20%), and hardware (19%)—all in the neighborhood of 20% growth.

Trends 134

Trends Technical Review Technology Software Review 134

Altexsoft

NOVEMBER 5, 2021

Among the top benefits of such systems are the adoption of various data formats and scalability, which means your HIS will cope with growing numbers of users and increased amounts of information. urrently, health information management as a discipline continues expanding — this time, towards Big Data and analytics.

Technical Review 59

Technical Review Technology Software Review Healthcare 59

O'Reilly Media - Ideas

JULY 15, 2020

Technical roles represented in the “Other” category include IT managers, data engineers, DevOps practitioners, data scientists, systems engineers, and systems administrators. Combined, technology verticals—software, computers/hardware, and telecommunications—account for about 35% of the audience (Figure 2).

Microservices 140

Microservices Weak Development Team Survey Architecture 140

Cloudera

MAY 3, 2021

Cloudera sees success in terms of two very simple outputs or results – building enterprise agility and enterprise scalability. data is generated – at the Edge. In the last five years, there has been a meaningful investment in both Edge hardware compute power and software analytical capabilities. A rare breed.

Scalability 100

Scalability Agile Data Systems Review 100

Kentik

DECEMBER 21, 2015

Under the hood, Kentik Detect is powered by Kentik Data Engine (KDE), a high-availability, massively-scalable, multi-tenant distributed database. Selecting the faster worker is complicated by the fact that workers run on heterogeneous hardware. Applying Multi-level Queues in Multi-tenant Databases.

Resources 40

Resources Hardware System Microservices 40

Coforge

AUGUST 20, 2019

Outsourcing: Some of the work related to data engineering and DevOps/SRE may be outsourced to concentrate resources towards achieving the business goals. #2 So, building a flexible and developer-friendly platform over an appropriate scalable infrastructure, should be the first priority.

Machine Learning 8

Machine Learning Artificial Inteligence Enterprise Software Review 8