Cloudera

DECEMBER 21, 2021

Since the release of Cloudera Data Engineering (CDE) more than a year ago , our number one goal was operationalizing Spark pipelines at scale with first class tooling designed to streamline automation and observability. Data pipelines are composed of multiple steps with dependencies and triggers. New in 2021.

Data Engineering 115

Data Engineering Technical Review Software Review Engineering 115

Perficient

FEBRUARY 13, 2023

Introduction: We often end up creating a problem while working on data. So, here are few best practices for data engineering using snowflake: 1.Transform So, resist the temptation to periodically load data using other methods (such as querying external tables). In fact, this is another example of using the right tools.

Data Engineering 69

Data Engineering Engineering Data Storage 69

CIO

JANUARY 19, 2024

Whether you’re looking to earn a certification from an accredited university, gain experience as a new grad, hone vendor-specific skills, or demonstrate your knowledge of data analytics, the following certifications (presented in alphabetical order) will work for you. Not finding what you’re looking for?

Artificial Inteligence 317

Artificial Inteligence Data Machine Learning Azure 317

Trigent

JUNE 20, 2022

To do this, they are constantly looking to partner with experts who can guide them on what to do with that data. This is where data engineering services providers come into play. Data engineering consulting is an inclusive term that encompasses multiple processes and business functions.

Data Engineering 52

Data Engineering Engineering Data Strategy 52

Cloudera

JUNE 26, 2023

Over time, using the wrong tool for the job can wreak havoc on environmental health. Here are some tips and tricks of the trade to prevent well-intended yet inappropriate data engineering and data science activities from cluttering or crashing the cluster. So which open source pipeline tool is better, NiFi or Airflow?

Tools 75

Tools Data Engineering Analytics Testing 75

Mobilunity

AUGUST 3, 2021

Big Data is a collection of data that is large in volume but still growing exponentially over time. It is so large in size and complexity that no traditional data management tools can store or manage it effectively. Who is Big Data Engineer? Big Data requires a unique engineering approach.

Big Data 52

Big Data Data Engineering Engineering Data 52

CIO

SEPTEMBER 2, 2022

The US financial services industry has fully embraced a move to the cloud, driving a demand for tech skills such as AWS and automation, as well as Python for data analytics, Java for developing consumer-facing apps, and SQL for database work. Data engineer.

Software Engineering 246

Software Engineering Data Engineering DevOps AWS 246

CIO

AUGUST 31, 2022

The US financial services industry has fully embraced a move to the cloud, driving a demand for tech skills such as AWS and automation, as well as Python for data analytics, Java for developing consumer-facing apps, and SQL for database work. Data engineer.

Software Engineering 246

Software Engineering Data Engineering DevOps AWS 246

Cloudera

SEPTEMBER 2, 2021

The shift to cloud has been accelerating, and with it, a push to modernize data pipelines that fuel key applications. That is why cloud native solutions which take advantage of the capabilities such as disaggregated storage & compute, elasticity, and containerization are more paramount than ever.

Data Engineering 81

Data Engineering Performance Engineering Data 81

Cloudera

DECEMBER 20, 2022

In this respect, several studies project that a proper use of advanced analytics implies savings of between 5% and 7.5%. For this reason, the need to have a data platform that allows for a fine-tuned analysis and prediction of supply and demand becomes a necessary tool to operate the business in a more efficient and profitable way.

Energy 79

Energy Analytics Data Machine Learning 79

CIO

FEBRUARY 14, 2023

This has also accelerated the execution of edge computing solutions so compute and real-time decisioning can be closer to where the data is generated. AI continues to transform customer engagements and interactions with chatbots that use predictive analytics for real-time conversations. report they have established a data culture 26.5%

Artificial Inteligence 290

Artificial Inteligence Strategy Data Machine Learning 290

CIO

FEBRUARY 13, 2023

This includes spending on strengthening cybersecurity (35%), improving customer service (32%) and improving data analytics for real-time business intelligence and customer insight (30%). On-prem infrastructure will grow cold — with the exception of storage, Nardecchia says.

Budget 363

Budget Artificial Inteligence Technical Review VR 363

Cloudera

JULY 24, 2023

In their effort to reduce their technology spend, some organizations that leverage open source projects for advanced analytics often consider either building and maintaining their own runtime with the required data processing engines or retaining older, now obsolete, versions of legacy Cloudera runtimes (CDH or HDP).

Open Source 65

Open Source Analytics Software Review Metrics 65

The Crazy Programmer

NOVEMBER 6, 2021

A cloud architect has a profound understanding of storage, servers, analytics, and many more. They also use tools like Amazon Web Services and Microsoft Azure. Big Data Engineer. Another highest-paying job skill in the IT sector is big data engineering. AI or Artificial Intelligence Engineer.

Artificial Inteligence 290

Artificial Inteligence Artificial Intelligence Blockchain Software Review 290

Altexsoft

SEPTEMBER 9, 2022

We’ll briefly recap the basics first and then discuss metadata management and tools that can come in handy. Metadata is basically information that describes other data. It helps us understand the origin, structure, nature, and context of data. It aims at making data assets understandable and discoverable for users.

Tools 59

Tools Technical Review Software Review Systems Review 59

Altexsoft

FEBRUARY 11, 2023

Data is now one of the most valuable assets for any kind of business. The 11th annual survey of Chief Data Officers (CDOs) and Chief Data and Analytics Officers reveals 82 percent of organizations are planning to increase their investments in data modernization in 2023. Feel free to enjoy it.

Data 87

Data Data Engineering Big Data Architecture 87

InnovationM

MAY 14, 2023

In this article, I will share practical insights and technologies utilized in building and harnessing the potential of data lakes. Demystifying Data Lakes Data lakes serve as flexible storage repositories, enabling organizations to store raw and diverse data types, breaking away from the constraints of traditional data warehouses.

Data 52

Data Storage Construction Business Intelligence 52

CIO

AUGUST 2, 2023

A data and analytics capability cannot emerge from an IT or business strategy alone. With both technology and business organization deeply involved in the what, why, and how of data, companies need to create cross-functional data teams to get the most out of it. This is where we select visualization tools, for example.

Data 246

Data Architecture Government Strategy 246

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

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

Kentik

APRIL 12, 2023

The DataOps ecosystem comprises several components, including people, processes, and tools. Data scientists play a critical role in the DataOps ecosystem, leveraging advanced analytics and machine learning techniques to gain insights from large and complex data sets. How is DataOps different from DevOps?

Network 52

Network Data Engineering Machine Learning Artificial Inteligence 52

Hu's Place - HitachiVantara

APRIL 11, 2019

Few Data Management Frameworks are Business Focused Data management has been around since the beginning of IT, and a lot of technology has been focused on big data deployments, governance, best practices, tools, etc. However, large data hubs over the last 25 years (e.g.,

Data Engineering 81

Data Engineering Machine Learning Artificial Inteligence Technical Review 81

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

JUNE 18, 2022

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. Data lakes and data warehouses unify large volumes and varieties of data into a central location.

Data 89

Data Analytics Open Source Architecture 89

Dzone - DevOps

FEBRUARY 16, 2023

Too Much Data on My Plate The choice of data warehouses was never high on my worry list until 2021. I have been working as a data engineer for a Fintech SaaS provider since its incorporation in 2014. In the company's infancy, we didn't have too much data to juggle. That was not good enough.

Data 71

Data ChatGPT Storage Architecture 71

O'Reilly Media - Ideas

JANUARY 8, 2019

From infrastructure to tools to training, Ben Lorica looks at what’s ahead for data. Whether you’re a business leader or a practitioner, here are key data trends to watch and explore in the months ahead. Increasing focus on building data culture, organization, and training.

Trends 87

Trends Data Machine Learning Artificial Inteligence 87

Confluent

FEBRUARY 6, 2019

It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers.

Machine Learning 106

Machine Learning Artificial Inteligence Scalability Data Engineering 106

O'Reilly Media - Data

APRIL 3, 2018

Strata Data London will introduce technologies and techniques; showcase use cases; and highlight the importance of ethics, privacy, and security. The growing role of data and machine learning cuts across domains and industries. Data Science and Machine Learning sessions will cover tools, techniques, and case studies.

Machine Learning 155

Machine Learning Artificial Inteligence Company Case Study 155

Hu's Place - HitachiVantara

MAY 10, 2019

For this reason, many financial institutions are converting their fraud detection systems to machine learning and advanced analytics and letting the data detect fraudulent activity. However, in the rush to do this, many of these systems have been poorly architected to address the total analytics pipeline.

Data 90

Data Machine Learning Artificial Inteligence Weak Development Team 90

Apps Associates

JULY 28, 2022

I love Data Integration in OCI and hope to get you excited about this great product as well. I will deep dive into the details and answer many questions about data integration. Oracle Cloud Infrastructure Data Integration is a next generation, fully managed, multi-tenant, serverless, native cloud ETL service. Oracle Managed.

Infrastructure 52

Infrastructure Cloud Data Linux 52

Cloudera

AUGUST 31, 2021

Data warehousing is the backbone of every data driven organization , providing mission critical analytics. Today, modern data warehousing has evolved to meet the intensive demands of the newest analytics required for a business to be data driven. Copyright © 2021 Accenture. All rights reserved.

Data 80

Data Analytics Cloud Technical Review 80

O'Reilly Media - Data

JULY 18, 2018

While models and algorithms garner most of the media coverage, this is a great time to be thinking about building tools in data. In this post I share slides and notes from a keynote I gave at the Strata Data Conference in London at the end of May. User expectations for what happens to data they generate has changed.

Machine Learning 137

Machine Learning Artificial Inteligence Data Marketing 137

Cloudera

JANUARY 15, 2021

Cloud data warehouses allow users to run analytic workloads with greater agility, better isolation and scale, and lower administrative overhead than ever before. As depicted in the chart, Cloudera Data Warehouse ran the benchmark with significantly better price-performance than any of the other competitors tested. Introduction.

Performance 90

Performance Cloud Data Storage 90

Existek

JANUARY 11, 2022

The cloud computing market covers many areas like business processes, infrastructure, platform, security, management, analytics supported by cloud providers. In 2010, they launched Windows Azure, the PaaS, positioning it as an alternative to Google App Engine and Amazon EC2. Data and analytics. Internet of Things.

Azure 52

Azure AWS Cloud How To 52

Cloudera

DECEMBER 10, 2020

Generally, if five LOB users use the data warehouse on a public cloud for eight hours a day for one month, you pay for the use of the service and the associated cloud hardware resources (compute and storage) for this period. 150 for storage use = $15 / TB / month x 10 TB. 150 for storage use = $15 / TB / month x 10 TB.

Data 98

Data Technical Review Storage Systems Review 98

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 103

Data Artificial Inteligence Analytics Machine Learning 103

Hu's Place - HitachiVantara

JANUARY 3, 2019

Public cloud, agile methodologies and devops, RESTful APIs, containers, analytics and machine learning are being adopted. ” Deployments of large data hubs have only resulted in more data silos that are not easily understood, related, or shared. Happy New Year and welcome to 2019, a year full of possibilities.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86