Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. These models may include regression, classification, clustering, and more.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Smart Data Collective

NOVEMBER 8, 2020

This explains the current surge in demand for data engineers, especially in data-driven companies. That said, if you are determined to be a data engineer , getting to know about big data and careers in big data comes in handy. Similarly, various tools used in data engineering revolve around Scala.

Data Engineer 104

Data Engineer Data Engineering Data Engineering Data Engineering 104

Mlearning.ai

MAY 16, 2023

Data engineering is a rapidly growing field that designs and develops systems that process and manage large amounts of data. There are various architectural design patterns in data engineering that are used to solve different data-related problems.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

AWS Machine Learning Blog

DECEMBER 18, 2023

Prerequisites For this solution we use MongoDB Atlas to store time series data, Amazon SageMaker Canvas to train a model and produce forecasts, and Amazon S3 to store data extracted from MongoDB Atlas. The following screenshots shows the setup of the data federation. Setup the Database access and Network access.

Clustering 122

Clustering AWS Database ML 122

Analytics Vidhya

DECEMBER 30, 2022

This article was published as a part of the Data Science Blogathon. Introduction Have you ever wondered how Instagram recommends similar kinds of reels while you are scrolling through your feed or ad recommendations for similar products that you were browsing on Amazon?

Apache Kafka 315

Apache Kafka Data Science Analytics Analytics 315

phData

NOVEMBER 8, 2024

Data Versioning and Time Travel Open Table Formats empower users with time travel capabilities, allowing them to access previous dataset versions. The first insert statement loads data having c_custkey between 30001 and 40000 – INSERT INTO ib_customers2 SELECT *, '11111111111111' AS HASHKEY FROM snowflake_sample_data.tpch_sf1.customer

Data Lakes 52

Data Lakes Data Warehouse Database Azure 52

Analytics Vidhya

APRIL 28, 2023

It is a famous Scala-coded data processing tool that offers low latency, extensive throughput, and a unified platform to handle the data in real-time. Introduction Apache Kafka is an open-source publish-subscribe messaging application initially developed by LinkedIn in early 2011.

Apache Kafka 289

Apache Kafka Analytics Analytics Hadoop 289

Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Dataconomy

JULY 24, 2023

Machine learning Machine learning is a key part of data science. It involves developing algorithms that can learn from and make predictions or decisions based on data. Familiarity with regression techniques, decision trees, clustering, neural networks, and other data-driven problem-solving methods is vital.

Data Scientist 172

Data Scientist Data Science Data Analyst Machine Learning 172

AWS Machine Learning Blog

FEBRUARY 13, 2024

Note the following calculations: The size of the global batch is (number of nodes in a cluster) * (number of GPUs per node) * (per batch shard) A batch shard (small batch) is a subset of the dataset assigned to each GPU (worker) per iteration BigBasket used the SMDDP library to reduce their overall training time.

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AWS Machine Learning Blog

MAY 16, 2024

With Amazon EMR, which provides fully managed environments like Apache Hadoop and Spark, we were able to process data faster. The data preprocessing batches were created by writing a shell script to run Amazon EMR through AWS Command Line Interface (AWS CLI) commands, which we registered to Airflow to run at specific intervals.

AWS 117

AWS ML ML Deep Learning 117

Pickl AI

JUNE 7, 2024

Integration: Airflow integrates seamlessly with other data engineering and Data Science tools like Apache Spark and Pandas. Oracle Data Integrator Oracle Data Integrator (ODI) is designed for building, deploying, and managing data warehouses. Read More: Advanced SQL Tips and Tricks for Data Analysts.

ETL 40

ETL Data Pipeline Data Quality Data Warehouse 40

DagsHub

OCTOBER 23, 2024

General Purpose Tools These tools help manage the unstructured data pipeline to varying degrees, with some encompassing data collection, storage, processing, analysis, and visualization. DagsHub's Data Engine DagsHub's Data Engine is a centralized platform for teams to manage and use their datasets effectively.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

ODSC - Open Data Science

JANUARY 7, 2025

Scala is worth knowing if youre looking to branch into data engineering and working with big data more as its helpful for scaling applications. Knowing all three frameworks covers the most ground for aspiring data science professionals, so you cover plenty of ground knowing thisgroup.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

phData

SEPTEMBER 20, 2023

phData has been working in data engineering since the inception of the company back in 2015. We have seen customers transform their data analytics with Snowflake and transform their data engineering and machine learning applications with Spark, Java, Scala, and Python.

SQL 98

SQL Python Data Lakes Machine Learning 98

Dataconomy

MARCH 19, 2025

Key disciplines involved in data science Understanding the core disciplines within data science provides a comprehensive perspective on the field’s multifaceted nature. Overview of core disciplines Data science encompasses several key disciplines including data engineering, data preparation, and predictive analytics.

Data Science 91

Data Science Citizen Data Scientist Data Scientist Machine Learning 91