Data Science Dojo

JULY 3, 2024

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Data Engineering : Building and maintaining data pipelines, ETL (Extract, Transform, Load) processes, and data warehousing.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Precisely

MARCH 9, 2023

Hadoop, Snowflake, Databricks and other products have rapidly gained adoption. We will also address some of the key distinctions between platforms like Hadoop and Snowflake, which have emerged as valuable tools in the quest to process and analyze ever larger volumes of structured, semi-structured, and unstructured data.

Data Lakes 59

Data Lakes Data Warehouse Hadoop Big Data 59

phData

AUGUST 9, 2023

This is unlike the more traditional ETL method, where data is transformed before loading into the data warehouse. By bringing raw data into the data warehouse and then transforming it there, ELT provides more flexibility compared to ETL’s fixed pipelines. ETL systems just couldn’t handle the massive flows of raw data.

ETL 52

ETL Data Warehouse Cloud Data Big Data 52

Pickl AI

OCTOBER 10, 2024

It integrates well with cloud services, databases, and big data platforms like Hadoop, making it suitable for various data environments. Typical use cases include ETL (Extract, Transform, Load) tasks, data quality enhancement, and data governance across various industries.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Pickl AI

NOVEMBER 4, 2024

Key components of data warehousing include: ETL Processes: ETL stands for Extract, Transform, Load. ETL is vital for ensuring data quality and integrity. Among these tools, Apache Hadoop, Apache Spark, and Apache Kafka stand out for their unique capabilities and widespread usage.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

IBM Journey to AI blog

JANUARY 18, 2023

While traditional data warehouses made use of an Extract-Transform-Load (ETL) process to ingest data, data lakes instead rely on an Extract-Load-Transform (ELT) process. This adds an additional ETL step, making the data even more stale. Data lakehouse was created to solve these problems. Data fabric: A mostly new architecture.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52

Pickl AI

APRIL 6, 2023

In-depth knowledge of distributed systems like Hadoop and Spart, along with computing platforms like Azure and AWS. This includes Database System Management (SQL or Non-SQL), Data Warehousing, Machine Learning, programming basics, and ETL. Sound knowledge of relational databases or NoSQL databases like Cassandra.

Azure 52

Azure Data Engineering Data Engineering Data Engineering 52

DagsHub

NOVEMBER 11, 2024

These capture the semantic relationships between words, facilitating tasks like classification and clustering within ETL pipelines. Multimodal embeddings help combine unstructured data from various sources in data warehouses and ETL pipelines. The features extracted in the ETL process would then be inputted into the ML models.

AI 52

AI AI Data Lakes Database 52

The MLOps Blog

JANUARY 23, 2023

It supports most major cloud providers, such as AWS, GCP, and Azure. datasets/images" ) In order to store artifacts from Amazon S3, we need to configure an IAM policy with “S3ReadAccessOnly” permissions and store our credentials for AWS as environment variables. Data versioning with DVC is very simple and straightforward.

ML 52

ML ML Data Lakes Machine Learning 52

DagsHub

OCTOBER 23, 2024

Popular data lake solutions include Amazon S3 , Azure Data Lake , and Hadoop. Apache Hadoop Apache Hadoop is an open-source framework that supports the distributed processing of large datasets across clusters of computers. is similar to the traditional Extract, Transform, Load (ETL) process. Unstructured.io

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

Pickl AI

MARCH 19, 2025

Apache Hive Apache Hive is a data warehouse tool that allows users to query and analyse large datasets stored in Hadoop. Talend Talend is a data integration tool that enables users to extract, transform, and load (ETL) data across different sources. Hadoop : An open-source framework for processing Big Data across multiple servers.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52