Confluent

FEBRUARY 6, 2019

This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. 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

CIO

JULY 14, 2023

To get good output, you need to create a data environment that can be consumed by the model,” he says. You need to have data engineering skills, and be able to recalibrate these models, so you probably need machine learning capabilities on your staff, and you need to be good at prompt engineering.

Generative AI 148

Generative AI Artificial Inteligence Open Source ChatGPT 148

Capgemini

OCTOBER 10, 2024

But gathering, analyzing, documenting, and structuring requirements can be tedious, and the results are often laden with errors. The traditional process for gathering requirements and documentation is manual, which makes it time-consuming and prone to inaccuracies, omissions, and inconsistencies.  Pro, a large language model (LLM).

Artificial Inteligence 52

Artificial Inteligence Analysis Google Cloud Infrastructure 52

Altexsoft

AUGUST 25, 2021

Both in daily life and in business, we deal with massive volumes of unstructured text data : emails, legal documents, product reviews, tweets, etc. Sentiment analysis results by Google Cloud Natural Language API. Intelligent document processing. Low-level vs high-level NLP tasks. Text classification. Source: IBM.

Tools 139

Tools Artificial Inteligence Technical Review Systems Review 139

Altexsoft

JANUARY 14, 2021

What specialists and their expertise level are required to handle a data warehouse? However, all of the warehouse products available require some technical expertise to run, including data engineering and, in some cases, DevOps. Data loading. Data loading. Is it a flat-rate or on-demand model? Integrations.

Backup 115

Backup Azure Software Review Architecture 115

Altexsoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Weak Development Team 64

Weak Development Team Artificial Inteligence Machine Learning Software Review 64

Xebia

JANUARY 9, 2024

Let’s imagine we are running dbt as a container within a cloud run job (a cloud-native container runtime within Google Cloud). Every morning when all the raw source data is ingested, we spin up a container via a trigger to do our daily data transformation workload using dbt.

Testing 130

Testing Data Open Source Applications 130

Altexsoft

DECEMBER 4, 2020

Three types of data migration tools. Automation scripts can be written by data engineers or ETL developers in charge of your migration project. This makes sense when you move a relatively small amount of data and deal with simple requirements. Phases of the data migration process. Data sources and destinations.

Software Review 69

Software Review Software Data Technical Review 69

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

UruIT

JULY 23, 2019

Having these requirements in mind and based on our own experience developing ML applications, we want to share with you 10 interesting platforms for developing and deploying smart apps: Google Cloud. MathWork focused on the development of these tools in order to become experts on high-end financial use and data engineering contexts.

Artificial Inteligence 81

Artificial Inteligence Machine Learning Azure Software Review 81

UruIT

APRIL 27, 2020

Google Cloud . MathWork focused on the development of these tools to become experts in high-end financial use and data engineering contexts. This company has jumped positions on Gartner’s list thanks to its innovative approach and thoughtful leadership in the form of content and documentation. . Algorithmia .

Artificial Inteligence 78

Artificial Inteligence Machine Learning Azure Software Review 78

Perficient

FEBRUARY 16, 2024

Reading Data: # Reading data from DBFS val data_df = spark.read.csv("dbfs:/FileStore/tables/Largest_earthquakes_by_year.csv") The code will read the specified CSV file into a DataFrame named data_df, allowing further processing and analysis using Spark’s DataFrame API. Databricks on AWS

System 52

System Storage Azure Big Data 52

Mobilunity

MARCH 19, 2025

Developers gather and preprocess data to build and train algorithms with libraries like Keras, TensorFlow, and PyTorch. Data engineering. Experts in the Python programming language will help you design, create, and manage data pipelines with Pandas, SQLAlchemy, and Apache Spark libraries. Creating cloud systems.

Trends 40

Trends Technical Review Development Software Review 40

Altexsoft

APRIL 26, 2022

Depending on the type and capacities of a warehouse, it can become home to structured, semi-structured, or unstructured data. Structured data is highly-organized and commonly exists in a tabular format like Excel files. As such, it is considered cloud-agnostic. Modern data pipeline with Snowflake technology as its part.

Weak Development Team 59

Weak Development Team Data Storage Technical Review 59

Exadel

FEBRUARY 20, 2023

SageMaker provides extensive documentation to help you understand how the algorithms work in the machine learning space. Vertex AI leverages a combination of data engineering, data science, and ML engineering workflows with a rich set of tools for collaborative teams.

Artificial Inteligence 52

Artificial Inteligence Cloud Machine Learning Azure 52

Altexsoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. cloud data warehouses — for example, Snowflake , Google BigQuery, and Amazon Redshift. Rich documentation, guides, and learning resources. Apache Kafka official documentation.

Weak Development Team 93

Weak Development Team Technical Review Systems Review Open Source 93

Abhishek Tiwari

DECEMBER 22, 2017

As you can see data transformation before the load is an important and necessary step in this classic ETL model, and with ELT approach we are making data transformation more on-demand. Using ELT, you can always create ad-hoc views by running the interactive queries and write results back to data lake. Late transformation.

Storage 40

Storage Big Data Google Cloud Analysis 40

Altexsoft

JULY 19, 2022

As the picture above clearly shows, organizations have data producers and operational data on the left side and data consumers and analytical data on the right side. Data producers lack ownership over the information they generate which means they are not in charge of its quality. It works like this.

Architecture 52

Architecture Data Analytics Data Engineering 52

Mobilunity

NOVEMBER 11, 2024

Google Cloud Certified: Machine Learning Engineer. The certification delivers expertise in Google Cloud’s machine learning tools, prioritizing building, training, and deployment of extensive models. The goal was to launch a data-driven financial portal. Here’s when LLM certifications occur.

Artificial Inteligence 52

Artificial Inteligence Handbook Engineering Technical Review 52

Existek

SEPTEMBER 28, 2023

Collaboration: They also collaborate with cross-functional teams, including data scientists, data engineers, software developers, and domain experts, to ensure that AI solutions align with organizational goals. The update with the latest trends and technologies in the AI field is also important.

Artificial Inteligence 52

Artificial Inteligence Development How To Technical Review 52

Altexsoft

DECEMBER 15, 2021

The rest is done by data engineers, data scientists , machine learning engineers , and other high-trained (and high-paid) specialists. The technology supports tabular, image, text, and video data, and also comes with an easy-to-use drag-and-drop tool to engage people without ML expertise. Source: Google Cloud Blog.

Machine Learning 59

Machine Learning Artificial Inteligence How To Open Source 59

Cloudera

DECEMBER 12, 2018

It’s certainly no longer like 2000 when every startup picked Oracle to run their back-end store for whatever site they were building — in 2018 there’s a variety of different database or data store engines. There’s MongoDB for document stores. Greg Rahn: Oh, definitely.

Marketing 42

Marketing Data Storage Big Data 42

O'Reilly Media - Ideas

JANUARY 14, 2025

However, Anthropics documentation is full of warnings about serious security vulnerabilities that remain to be solved. Building applications with RAG requires a portfolio of data (company financials, customer data, data purchased from other sources) that can be used to build queries, and data scientists know how to work with data at scale.

Trends 130

Trends Technology Security Artificial Inteligence 130

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. How data engineering works under the hood.

Big Data 59

Big Data Data Google Cloud Open Source 59

Altexsoft

NOVEMBER 7, 2022

You can hardly compare data engineering toil with something as easy as breathing or as fast as the wind. The platform went live in 2015 at Airbnb, the biggest home-sharing and vacation rental site, as an orchestrator for increasingly complex data pipelines. How data engineering works. What is Apache Airflow?

Weak Development Team 52

Weak Development Team Technical Review Software Review Data Engineering 52

CIO

JANUARY 29, 2025

Large enterprises have long used knowledge graphs to better understand underlying relationships between data points, but these graphs are difficult to build and maintain, requiring effort on the part of developers, data engineers, and subject matter experts who know what the data actually means.

Artificial Inteligence 202

Artificial Inteligence Enterprise Open Source Research 202