Cloudera

FEBRUARY 1, 2024

Day 0 — Design and Preparation: Focuses on designing and preparing for your installation, including gathering requirements, planning architecture, allocating resources, setting up network and security, and documentation creation. How does Cloudera support Day 2 operations?

Backup 82

Backup Cloud Architecture Resources 82

Altexsoft

MAY 27, 2020

But, in any case, the pipeline would provide data engineers with means of managing data for training, orchestrating models, and managing them on production. Data gathering : Collecting the required data is a beginning of the whole process. Machine learning production pipeline architecture.

Machine Learning 89

Machine Learning Artificial Inteligence Architecture Training 89

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 120

Artificial Inteligence Generative AI Security Applications 120

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 97

Off-The-Shelf Data Engineering Virtualization Cloud 97

Erik Bernhardsson

DECEMBER 15, 2020

I had my first job as a software engineer in 1999, and in the last two decades I've seen software engineering changing in ways that have made us orders of magnitude more productive. Decades ago, software engineering was hard because you had to build everything from scratch and solve all these foundational problems.

Software Engineering 304

Software Engineering Engineering Tools Software 304

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 81

Data Analytics Cloud Technical Review 81

Altexsoft

SEPTEMBER 9, 2022

Metadata management is a set of activities, technologies, and policies that target metadata collection, storage, and organizing. It aims at making data assets understandable and discoverable for users. Plus, a data fabric architecture design approach is also based on metadata as one of the main building blocks.

Tools 59

Tools Technical Review Software Review Systems Review 59

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.

Data Engineering 98

Data Engineering Engineering Data Machine Learning 98

Cloudera

FEBRUARY 14, 2024

Introduction Apache Iceberg has recently grown in popularity because it adds data warehouse-like capabilities to your data lake making it easier to analyze all your data — structured and unstructured. 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

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

JUNE 25, 2019

If we look at the hierarchy of needs in data science implementations, we’ll see that the next step after gathering your data for analysis is data engineering. This discipline is not to be underestimated, as it enables effective data storing and reliable data flow while taking charge of the infrastructure.

Data Engineering 106

Data Engineering Engineering Data Artificial Inteligence 106

Kentik

APRIL 11, 2016

Clustered computing for real-time Big Data analytics. The concept of parallel processing based on a “clustered” multi-computer architecture has a long history dating back at least as far as Gene Amdahl’s work at IBM in the 1960s. For more on how we make it work, see Inside the Kentik Data Engine.).

Big Data 40

Big Data Analytics Network Architecture 40

Cloudera

MARCH 23, 2022

Please join us on March 24 for Future of Data meetup where we do a deep dive into Iceberg with CDP . Apache Iceberg is a high-performance, open table format, born-in-the cloud that scales to petabytes independent of the underlying storage layer and the access engine layer. What is Apache Iceberg? 1: Multi-function analytics .

Data 90

Data Open Source Storage Machine Learning 90

Altexsoft

JANUARY 14, 2021

Snowflake, Redshift, BigQuery, and Others: Cloud Data Warehouse Tools Compared. From simple mechanisms for holding data like punch cards and paper tapes to real-time data processing systems like Hadoop, data storage systems have come a long way to become what they are now. Data warehouse architecture.

Backup 115

Backup Azure Software Review Systems Review 115

Mobilunity

NOVEMBER 24, 2021

In most digital spheres, especially in fintech, where all business processes are tied to data processing, a good big data engineer is worth their weight in gold. In this article, we’ll discuss the role of an ETL engineer in data processing and why businesses need such experts nowadays. Who Is an ETL Engineer?

Development 52

Development Storage Recruiting Architecture 52

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

Altexsoft

APRIL 26, 2022

We’ll dive deeper into Snowflake’s pros and cons, its unique architecture, and its features to help you decide whether this data warehouse is the right choice for your company. Data warehousing in a nutshell. The data journey from different source systems to a warehouse commonly happens in two ways — ETL and ELT.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Altexsoft

MARCH 16, 2021

In short, this term relates to large amounts of data — both structured and unstructured — that are too voluminous or complex to be processed with traditional software tools. Having described its main characteristics, it becomes clear that not all medical information can be referred to as big data.

Big Data 116

Big Data Healthcare Applications Data 116

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. Apache Hadoop architecture. What is Hadoop?

Big Data 59

Big Data Data Google Cloud Storage 59

Altexsoft

JUNE 26, 2023

Both data integration and ingestion require building data pipelines — series of automated operations to move data from one system to another. For this task, you need a dedicated specialist — a data engineer or ETL developer. Data engineering explained in 14 minutes. Find sources of relevant data.

Machine Learning 52

Machine Learning Artificial Inteligence Data Systems Review 52

Kentik

FEBRUARY 7, 2023

But what are network operators to do when their cloud networks have to be distributed, both architecturally and geographically? Suddenly, this dense data mass is a considerable burden, and the same forces that happily drew in service and customer data find that that data is now trapped and extremely expensive and complicated to move.

Network 59

Network Cloud Data Data Center 59

Abhishek Tiwari

DECEMBER 27, 2017

ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. There are several emerging data trends that will define the future of ETL in 2018. A common theme across all these trends is to remove the complexity by simplifying data management as a whole.

Trends 49

Trends Artificial Inteligence Data Big Data 49

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

DECEMBER 7, 2017

This means cloud makes it too easy to accidentally create a lot of redundant silos of data for each of those cloud analytics services. That data may be stored multiple times in different pools, each multiplying storage resource costs. That data may be hard to discover for other users and other applications.

Data 40

Data Machine Learning Artificial Inteligence Analytics 40

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. Virtually, Hadoop puts no limits on the storage capacity. What is Hadoop.

Big Data 98

Big Data Tools Data Storage 98

Altexsoft

NOVEMBER 9, 2021

Purchasing refers to actually buying the goods, i.e., issuing purchase orders (POs), receiving items, and inspecting them. Dark or maverick spending refers to purchases made outside the defined procurement procedure without following pre-established policies. It includes evaluating vendors, negotiating terms, managing contracts, etc.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Altexsoft

MARCH 2, 2023

Whether you’re just getting started with data governance or looking to refine your existing program, this article will provide you with valuable insights and practical tips to help you manage your data more effectively. What is data governance? Its goal is to define and control all data governance initiatives.

Government 52

Government Tools Data Weak Development Team 52

Altexsoft

AUGUST 4, 2021

Depending on a goal, it may take weeks or months to set up a data lake. Moreover, not all organizations use data lakes. Data mart vs data warehouse vs data lake architectures. Data marts shouldn’t be confused with OLAP cubes either. Virtual data marts may be a good option when resources are limited.

Data 64

Data Analytics Construction Cloud 64

Altexsoft

MAY 14, 2021

Not only does Big Data apply to the huge volumes of continuously growing data that come in different formats, but it also refers to the range of processes, tools, and approaches used to gain insights from that data. Key Big Data characteristics. What is Big Data analytics? Data storage and processing.

Big Data 78

Big Data Analytics Tools Applications 78

Cloudera

SEPTEMBER 10, 2018

Multidisciplinary analytics are the tools, the different workloads you need: data engineering, data warehousing, data science, and operational analytics. Therefore, it’s crucial that we provide complete consistency across the entire realm of data context: security, governance, lifecycle, control and data catalog.

Analytics 40

Analytics Machine Learning Artificial Inteligence Cloud 40

Altexsoft

FEBRUARY 22, 2022

As we discussed in one of our previous articles, there are three main types of maintenance policies : reactive, preventive, and predictive (the two latter categories are referred to as proactive maintenance). Processing data. Telematics data is generated continuously in enormous amounts so it falls under the category of big data.

Software Review 52

Software Review Technical Review Software Technology 52

Daniel Bryant

SEPTEMBER 13, 2019

What was worth noting was that (anecdotally) even engineers from large organisations were not looking for full workload portability (i.e. There were also two patterns of adoption of HashiCorp tooling I observed from engineers that I chatted to: Infrastructure-driven?—?in

Infrastructure 8

Infrastructure Azure Software Engineering Cloud 8

Altexsoft

NOVEMBER 5, 2021

Health information vs health data. Terms data and information are often used interchangeably but HIM strictly differentiates them. Data refers to raw facts and figures. By contrast, health information (HI) means knowledge obtained after data is processed and structured into a meaningful form.

Technical Review 59

Technical Review Technology Software Review Healthcare 59

Confluent

OCTOBER 16, 2019

With tools like KSQL and Kafka Connect, the concept of streaming ETL is made accessible to a much wider audience of developers and data engineers. My source of data is a public feed provided by the UK’s Network Rail company through an ActiveMQ interface. There’s also some static reference data that is published on web pages. ?After

Data 19

Data Training Analytics Storage 19

Kentik

JUNE 4, 2019

For the ISP’s executive and engineering teams, these variations increase the complexity of meeting two main operational goals: Ensuring the high throughput required for a good user experience. To succeed at these goals, an ISP needs to define a robust, performant architecture and also a set of best practices.

Video 56

Video Network Internet Groups 56

Altexsoft

DECEMBER 9, 2020

So, the path that companies cover in their analytical development can be broken down into 5 stages: No analytics refers to companies with no analytical processes whatsoever. Descriptive analytics lets us know what happened , gathering, and visualizing historical data. Data warehouse architecture.

Analytics 102

Analytics Technical Review Technology Applications 102

Altexsoft

SEPTEMBER 20, 2019

Analyzing business information to facilitate data-driven decision making is what we call business intelligence or BI. In plain language, BI is a set of tools and methods to extract raw data from its source, transform it, load into a unified storage, and present to the user. Architecture of your database/data warehouse.

Business Intelligence 18

Business Intelligence Tools Data Analytics 18