Confluent

SEPTEMBER 26, 2019

® , Go, and Python SDKs where an application can use SQL to query raw data coming from Kafka through an API (but that is a topic for another blog). Let’s now dig a little bit deeper into Kafka and Rockset for a concrete example of how to enable real-time interactive queries on large datasets, starting with Kafka.

Kafka 21

Kafka SQL BI Hadoop 21

LinkedIn Engineering

APRIL 4, 2023

This blog post will provide an overview of how we approached metrics selection and design, system architecture and key product features. raw data path, column mappings, aggregation function to be used, etc.) Here is an example of a metric configuration file that describes how a metric dataset should be processed.

MySQL 122

MySQL Datasets Software Engineer Software Engineering 122

Cloudyard

DECEMBER 24, 2024

Snowflakes Snowpark is a game-changing feature that enables data engineers and analysts to write scalable data transformation workflows directly within Snowflake using Python, Java, or Scala. They need to: Consolidate raw data from orders, customers, and products. Enrich and clean data for downstream analytics.

Building 52

Building Raw Data Scala Business Intelligence 52

Workfall

SEPTEMBER 18, 2023

Pair this with Snowflake , the cloud data warehouse that acts as a vault for your insights, and you have a recipe for data-driven success. Get ready to explore the realm where data dreams become reality! In this blog, we will cover: What is Airbyte? With Airbyte and Snowflake, data integration is now a breeze.

Data Pipeline 93

Data Pipeline Raw Data Data Schemas Healthcare 93

DataKitchen

DECEMBER 9, 2022

DataOps involves collaboration between data engineers, data scientists, and IT operations teams to create a more efficient and effective data pipeline, from the collection of raw data to the delivery of insights and results. Query> An AI, Chat GPT wrote this blog post, why should I read it? .

Machine Learning 52

Machine Learning Data Preparation Government Data Analytics 52

Workfall

JULY 18, 2023

Behind the scenes, a team of data wizards tirelessly crunches mountains of data to make those recommendations sparkle. As one of those wizards, we’ve seen the challenges we face: the struggle to transform massive datasets into meaningful insights, all while keeping queries fast and our system scalable.

Datasets 77

Datasets Entertainment Data Workflow Data 77

Ascend.io

OCTOBER 28, 2024

What is Data Transformation? Data transformation is the process of converting raw data into a usable format to generate insights. It involves cleaning, normalizing, validating, and enriching data, ensuring that it is consistent and ready for analysis.

Raw Data 52

Raw Data Datasets Aggregated Data Data Pipeline 52

Knowledge Hut

NOVEMBER 27, 2023

In fact, you reading this blog is also being recorded as an instance of data in some digital storage. In 2018, the world produced 33 Zettabytes (ZB) of data, which is equivalent to 33 trillion Gigabytes (GB). Learn how to import data, to visualize data using libraries like Matplotlib and Seaborn.

Data Science 97

Data Science Healthcare Machine Learning Algorithm 97

Maxime Beauchemin

JANUARY 7, 2018

While business rules evolve constantly, and while corrections and adjustments to the process are more the rule than the exception, it’s important to insulate compute logic changes from data changes and have control over all of the moving parts. Late arriving facts Late arriving facts can be problematic with a strict immutable data policy.

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Data Process Data Engineering Data Engineer Process 183

DataKitchen

MAY 10, 2024

The Five Use Cases in Data Observability: Mastering Data Production (#3) Introduction Managing the production phase of data analytics is a daunting challenge. Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs. Have I Checked The Raw Data And The Integrated Data?

Raw Data 53

Raw Data Data Ingestion Datasets Data 53

Cloudera

DECEMBER 17, 2020

First and foremost, we designed the Cloudera Data Platform (CDP) to optimize every step of what’s required to go from raw data to AI use cases. In August 2020 we released CDP Data Engineering (DE) — our answer to enabling fast, optimized, and automated data engineering for analytic workloads.

Machine Learning 82

Machine Learning Data Science Data Pipeline Raw Data 82

Lyft Engineering

APRIL 25, 2023

Building a large scale unsupervised model anomaly detection system — Part 2 Building ML Models with Observability at Scale By Rajeev Prabhakar , Han Wang , Anindya Saha Photo by Octavian Rosca on Unsplash In our previous blog we discussed the different challenges we faced for model monitoring and our strategy for addressing some of these problems.

Systems 78

Systems Building Machine Learning Datasets 78

ProjectPro

FEBRUARY 8, 2023

Do ETL and data integration activities seem complex to you? Read this blog to understand everything about AWS Glue that makes it one of the most popular data integration solutions in the industry. Did you know the global big data market will likely reach $268.4 Businesses are leveraging big data now more than ever.

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AWS Scala Metadata Data Lake 98

Databand.ai

AUGUST 30, 2023

Data testing tools: Key capabilities you should know Helen Soloveichik August 30, 2023 Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing and maintaining data quality. There are several types of data testing tools.

Data Cleanse 52

Data Cleanse Data Pipeline Datasets Data Validation 52

Striim

SEPTEMBER 11, 2024

Third-Party Data: External data sources that your company does not collect directly but integrates to enhance insights or support decision-making. These data sources serve as the starting point for the pipeline, providing the raw data that will be ingested, processed, and analyzed.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

AltexSoft

AUGUST 3, 2021

Now, the primary function of data labeling is tagging objects on raw data to help the ML model make accurate predictions and estimations. That said, data annotation is key in training ML models if you want to achieve high-quality outputs. This blog entry is particularly helpful to anyone who wants to.

Datasets 59

Datasets High Quality Data Metadata Raw Data 59

Knowledge Hut

JULY 28, 2023

Data Labeling is the process of assigning meaningful tags or annotations to raw data, typically in the form of text, images, audio, or video. These labels provide context and meaning to the data, enabling machine learning algorithms to learn and make predictions. What is Data Labeling for Machine Learning?

Machine Learning 52

Machine Learning Process Datasets Raw Data 52

Databand.ai

JULY 6, 2023

7 Data Pipeline Examples: ETL, Data Science, eCommerce, and More Joseph Arnold July 6, 2023 What Are Data Pipelines? Data pipelines are a series of data processing steps that enable the flow and transformation of raw data into valuable insights for businesses.

Data Pipeline 52

Data Pipeline Data Science Raw Data Media 52

Databand.ai

AUGUST 30, 2023

Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing, and maintaining data quality. There are several types of data testing tools. Data profiling tools: Profiling plays a crucial role in understanding your dataset’s structure and content.

Data Cleanse 52

Data Cleanse Data Validation Data Pipeline Datasets 52

Workfall

JULY 4, 2023

Reading Time: 8 minutes In the world of data engineering, a mighty tool called DBT (Data Build Tool) comes to the rescue of modern data workflows. Imagine a team of skilled data engineers on an exciting quest to transform raw data into a treasure trove of insights. Happy DBT-ing!

Data Warehouse 69

Data Warehouse SQL Database PostgreSQL 69

Edureka

JANUARY 2, 2025

Power BI’s extensive modeling, real-time high-level analytics, and custom development simplify working with data. You will often need to work around several features to get the most out of business data with Microsoft Power BI. Additionally, it manages sizable datasets without causing Power BI to crash or perform less quickly.

BI 52

BI Datasets Business Intelligence Data Analysis 52

Striim

NOVEMBER 17, 2023

Section 1: Connecting to the Source Database The first step in this integration journey is connecting Striim to a PostgreSQL source database that contains raw machine learning data. In this blog, we will focus on a PostgreSQL database. It evaluates the model’s predictions against the actual values in the test dataset.

Machine Learning 52

Machine Learning Data Process PostgreSQL Process 52

DareData

JULY 5, 2023

If you work at a relatively large company, you've seen this cycle happening many times: Analytics team wants to use unstructured data on their models or analysis. For example, an industrial analytics team wants to use the logs from raw data. Understanding the Architecture No company is alike and no infrastructure will be alike.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Monte Carlo

FEBRUARY 9, 2023

In fact, with increasingly strict data regulations like GDPR and a renewed emphasis on optimizing technology costs, we’re now seeing a revitalization of “ Data Vault 2.0 ” data modeling. While data vault has many benefits, it is a sophisticated and complex methodology that can present challenges to data quality.

Architecture 52

Architecture Raw Data Metadata Data Warehouse 52

Edureka

AUGUST 2, 2023

Raw data, however, is frequently disorganised, unstructured, and challenging to work with directly. Data processing analysts can be useful in this situation. Let’s take a deep dive into the subject and look at what we’re about to study in this blog: Table of Contents What Is Data Processing Analysis?

Data Process 52

Data Process Process Data Cleanse Data Mining 52

ProjectPro

JANUARY 19, 2022

Source: Image uploaded by Tawfik Borgi on (researchgate.net) So, what is the first step towards leveraging data? The first step is to work on cleaning it and eliminating the unwanted information in the dataset so that data analysts and data scientists can use it for analysis.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Knowledge Hut

NOVEMBER 27, 2023

A data scientist’s job needs loads of exploratory data research and analysis on a daily basis with the help of various tools like Python, SQL, R, and Matlab. This role is an amalgamation of art and science that requires a good amount of prototyping, programming and mocking up of data to obtain novel outcomes.

Data Science 52

Data Science Datasets Machine Learning Algorithm 52

Knowledge Hut

JANUARY 24, 2024

Join me on this captivating expedition as we peel back the curtain, revealing the intricacies that define "A Day in the Life of a Data Scientist." This blog offers an exclusive glimpse into the daily rituals, challenges, and moments of triumph that punctuate the professional journey of a data scientist.

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Database-centric Data Science Machine Learning Algorithm 52

DataKitchen

MAY 4, 2023

Data Mesh Bergh explained that the Data Mesh organizes a team’s work into chunks called decentralized domains. Instead of boiling the ocean and focusing on all datasets and customers, the Data Mesh focuses on fewer datasets and customers, which reduces complexity and helps get more done.

Raw Data 52

Raw Data Data Datasets Data Engineering 52

AltexSoft

MAY 14, 2021

As you now know the key characteristics, it gets clear that not all data can be referred to as Big Data. What is Big Data analytics? Big Data analytics is the process of finding patterns, trends, and relationships in massive datasets that can’t be discovered with traditional data management techniques and tools.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. A pipeline may include filtering, normalizing, and data consolidation to provide desired data.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

AltexSoft

JANUARY 26, 2022

For more detailed information on data science team roles, check our video. An analytics engineer is a modern data team member that is responsible for modeling data to provide clean, accurate datasets so that different users within the company can work with them. Data modeling. What is an analytics engineer?

Engineering 59

Engineering Software Engineer Software Engineering Data Warehouse 59

Ascend.io

APRIL 5, 2023

For analytics engineers, understanding the business needs and transforming the data to meet them are two key steps. As most experienced data teams can tell you, simply connecting raw data sources to BI tools doesn’t get the job done. A data analyst at one company could be a BI engineer at another.”

Engineering 52

Engineering Raw Data BI Software Engineer 52

Cloudera

SEPTEMBER 13, 2018

Feature engineering, or the process of transforming raw data into a chosen representation of the underlying problem to be solved, is the data science process most ripe for automation. Before any machine learning can be applied to a business problem, data scientists need to define the problem itself.

Machine Learning 40

Machine Learning Engineering Raw Data Data Science 40

Knowledge Hut

APRIL 25, 2023

You can find a comprehensive guide on how data ingestion impacts a data science project with any Data Science course. Why Data Ingestion is Important? Data ingestion provides certain benefits to the business: The raw data coming from various sources is highly complex. Why Data Ingestion is Important?

Data Ingestion 52

Data Ingestion Lambda Architecture Raw Data Data Science 52

Picnic Engineering

JULY 24, 2023

In this blog post, we’ll discuss how we teach transformers to distinguish between products like a potato and a banana, thereby enhancing future demand prediction. By converting raw data into valuable information, transformer models could significantly contribute to sustainability. Then you are in the right place!

Datasets 52

Datasets Architecture Utilities Machine Learning 52

Knowledge Hut

MARCH 18, 2024

Consequently, the demand for professionals in both data analytics and project management remains exceedingly high in today's business landscape. As a data analyst, I focus on diving into data to draw out valuable insights, while project managers are all about leading and coordinating projects.

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Data Analytics Project Management Programming Language 52