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

Cloudera

SEPTEMBER 29, 2020

Performance is one of the key, if not the most important deciding criterion, in choosing a Cloud Data Warehouse service. In today’s fast changing world, enterprises have to make data driven decisions quickly and for that they rely heavily on their data warehouse service. . Cloudera Data Warehouse vs HDInsight.

Azure 115

Azure Data Comparison Virtualization 115

Exadel

FEBRUARY 20, 2023

Amazon For Cloud Artificial Intelligence Amazon began by making storage and virtual machines. Vertex AI leverages a combination of data engineering, data science, and ML engineering workflows with a rich set of tools for collaborative teams. Let’s race through these vendors and see what they’re up to.

Artificial Inteligence 52

Artificial Inteligence Cloud Machine Learning Azure 52

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 89

Data Analytics Open Source Architecture 89

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

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 93

Off-The-Shelf Data Engineering Virtualization Cloud 93

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

d2iq

FEBRUARY 19, 2021

Components that are unique to data engineering and machine learning (red) surround the model, with more common elements (gray) in support of the entire infrastructure on the periphery. Before you can build a model, you need to ingest and verify data, after which you can extract features that power the model.

Artificial Inteligence 103

Artificial Inteligence Machine Learning Technical Review Software Review 103

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

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. This will require another product for data governance. Data Preparation : Data integrationthat is intuitive and powerful.

Data 90

Data Machine Learning Artificial Inteligence Weak Development Team 90

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. For now, we’ll focus on Kafka.

Machine Learning 107

Machine Learning Artificial Inteligence Scalability Data Engineering 107

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

Kentik

MAY 23, 2017

Imagine a big data time-series datastore that unifies traffic flow records (NetFlow, sFlow, IPFIX) with related data such as BGP routing, GeoIP, network performance, and DNS logs, that retains unsummarized data for months, and that has the compute and storage power to answer ad hoc queries across billions of data points in a couple of seconds.

Network 40

Network Open Source Big Data Load Balancer 40

AWS Machine Learning - AI

JANUARY 26, 2024

We are committed to expanding our capabilities to allow you to meet your digital sovereignty needs, without compromising on the performance, innovation, security, or scale of the AWS Cloud. Understand the security posture of your AI, ML, and generative AI workloads by performing a Well-Architected Framework review.

Artificial Inteligence 117

Artificial Inteligence Generative AI Security Applications 117

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. One of the key requirements to the ML pipeline is to have control over the models, their performance, and updates. Getting additional data from feature store.

Machine Learning 89

Machine Learning Artificial Inteligence Architecture Training 89

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

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. You need to configure the backup repository in solr xml to point to your cloud storage location (in this example your S3 bucket). Prerequisites.

Data 66

Data Backup Disaster Recovery Storage 66

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

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

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

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. 2: Open formats.

Data 88

Data Open Source Storage Machine Learning 88

Cloudera

MAY 22, 2018

With CDSW, organizations can research and experiment faster, deploy models easily and with confidence, as well as rely on the wider Cloudera platform to reduce the risks and costs of data science projects. track model metrics, performance, and any model artifacts the user specifies.

Data 41

Data Load Balancer Machine Learning Artificial Inteligence 41

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 15

AWS Open Source Linux Engineering Management 15

Kentik

APRIL 11, 2016

Developed as a model for “processing and generating large data sets,” MapReduce was built around the core idea of using a map function to process a key/value pair into a set of intermediate key/value pairs, and then a reduce function to merge all intermediate values associated with a given intermediate key. The limits of Hadoop.

Big Data 40

Big Data Analytics Network Architecture 40

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. Stream processing.

Analytics 20

Analytics Data IoT Analysis 20

CIO

JUNE 14, 2023

Start with storage. Before you can even think about analyzing exabytes worth of data, ensure you have the infrastructure to store more than 1000 petabytes! Going from 250 PB to even a single exabyte means multiplying storage capabilities four times. So, what does it require for organizations to go from PB to EB scale?

Data 163

Data Scalability Storage Big Data 163

Cloudera

OCTOBER 7, 2022

dbt allows data teams to produce trusted data sets for reporting, ML modeling, and operational workflows using SQL, with a simple workflow that follows software engineering best practices like modularity, portability, and continuous integration/continuous development (CI/CD). Introduction. dbt-impala . dbt-spark-livy.

SDLC 79

SDLC Data Open Source Handbook 79

Altexsoft

JULY 18, 2023

Maintained by the Apache Software Foundation, Apache Spark is an open-source, unified engine designed for large-scale data analytics. With its native support for in-memory distributed processing and fault tolerance, Spark empowers users to build complex, multi-stage data pipelines with relative ease and efficiency.

Weak Development Team 52

Weak Development Team Big Data Data Machine Learning 52

Cloudera

APRIL 26, 2023

At its core, CDP Private Cloud Data Services (“the platform”) is an end-to-end cloud native platform that provides a private open data lakehouse. It offers features such as data ingestion, storage, ETL, BI and analytics, observability, and AI model development and deployment. What is cloud native exactly?

Cloud 63

Cloud Data Load Balancer Storage 63

O'Reilly Media - Ideas

MARCH 31, 2020

The biggest problems arise from taking shortcuts and assuming that a machine learning model that works for one application will perform well in a different context without looking at the underlying data. The prospect of taking on a costly data infrastructure project is daunting. Organizational prerequisites for AI at scale.

Product Management 138

Product Management Artificial Inteligence Machine Learning Software Review 138

Cloudera

JULY 13, 2023

A range of Iceberg table analysis such as listing table’s data file, selecting table snapshot, partition filtering, and predicate filtering can be delegated through Iceberg Java API instead, obviating the need for each query engine to implement it themself. However, Iceberg Java API calls are not always cheap.

Weak Development Team 69

Weak Development Team Engineering Analytics Storage 69

Cloudera

APRIL 22, 2021

Today’s enterprise data analytics teams are constantly looking to get the best out of their platforms. 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. Supports Disaggregation of compute and storage.

Data 98

Data Storage Architecture Big Data 98

Cloudera

FEBRUARY 22, 2022

This unprecedented level of big data workloads hasn’t come without its fair share of challenges. The data architecture layer is one such area where growing datasets have pushed the limits of scalability and performance. Apache Iceberg is a new open table format targeted for petabyte-scale analytic datasets.

Data 103

Data Analytics Disaster Recovery Travel 103

Altexsoft

APRIL 16, 2021

Later, this data can be: modified to maintain the relevance of what was stored, used by business applications to perform its functions, for example check product availability, etc. Namely, we’ll explain what functions it can perform, and how to use it for data analysis. An overview of data warehouse types.

Analytics 98

Analytics Analysis Storage Business Intelligence 98

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. What is Hadoop? Cost efficiency.

Big Data 59

Big Data Data Google Cloud Storage 59

Xebia

JANUARY 9, 2024

This gives some high-level information, however it is hard to determine what the actual SQL is that was executed, and on which exact records a data quality test failed or raised a warning. Logs will purely show information about a single run, so you don’t have an easy way to see how a test performs over time.

Testing 130

Testing Data Open Source Google Cloud 130