AWS, ETL and Hadoop - Data Science Current

Understanding ETL Tools as a Data-Centric Organization

Smart Data Collective

SEPTEMBER 8, 2021

The ETL process is defined as the movement of data from its source to destination storage (typically a Data Warehouse) for future use in reports and analyzes. Understanding the ETL Process. Before you understand what is ETL tool , you need to understand the ETL Process first. Types of ETL Tools.

ETL

ETL Hadoop Data Warehouse Data Pipeline

How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

FEBRUARY 21, 2025

Rockets legacy data science environment challenges Rockets previous data science solution was built around Apache Spark and combined the use of a legacy version of the Hadoop environment and vendor-provided Data Science Experience development tools. With the volume of business we do, even small improvements can have a significant impact.

Data Science

Data Science AWS Hadoop Data Scientist

Essential data engineering tools for 2023: Empowering for management and analysis

Data Science Dojo

JULY 6, 2023

These tools provide data engineers with the necessary capabilities to efficiently extract, transform, and load (ETL) data, build data pipelines, and prepare data for analysis and consumption by other applications. Amazon Redshift: Amazon Redshift is a cloud-based data warehousing service provided by Amazon Web Services (AWS).

Data Engineering

Data Engineering Data Engineer Data Engineering Data Engineering

Webinars

How to Achieve High-Accuracy Results When Using LLMs

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Remote Data Science Jobs: 5 High-Demand Roles for Career Growth

Data Science Dojo

OCTOBER 31, 2024

Strong analytical skills and the ability to work with large datasets are critical, as is familiarity with data modeling and ETL processes. Additionally, knowledge of cloud platforms (AWS, Google Cloud) and experience with deployment tools (Docker, Kubernetes) are highly valuable.

Data Science

Data Science Data Scientist Machine Learning Machine Learning

Top ETL Tools: Unveiling the Best Solutions for Data Integration

Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Choosing the right ETL tool is crucial for smooth data management.

ETL

ETL Data Quality Data Pipeline Data Warehouse

Navigating the Big Data Frontier: A Guide to Efficient Handling

Women in Big Data

OCTOBER 9, 2024

Big data pipelines operate similarly to traditional ETL (Extract, Transform, Load) pipelines but are designed to handle much larger data volumes. Data Ingestion: Data is collected and funneled into the pipeline using batch or real-time methods, leveraging tools like Apache Kafka, AWS Kinesis, or custom ETL scripts.

Big Data

Big Data Big Data Apache Kafka Data Pipeline

A Guide to Choose the Best Data Science Bootcamp

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

Data Science Machine Learning Machine Learning Data Visualization

Data Warehouse vs. Data Lake

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 Warehouse

Data Warehouse Data Lakes Hadoop Big Data

How Fivetran and dbt Help With ELT

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

ETL Data Warehouse Cloud Data Big Data

Popular Data Transformation Tools: Importance and Best Practices

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

Data Quality AWS Machine Learning Machine Learning

The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

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

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Discover the Most Important Fundamentals of Data Engineering

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

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Data platform trinity: Competitive or complementary?

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

Data Lakes Data Warehouse Azure Apache Hadoop

Azure Data Engineer Jobs

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

Azure Data Engineering Data Engineer Data Engineering

How to Effectively Handle Unstructured Data Using AI

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

AI AI Data Lakes Database

How to Version Control Data in ML for Various Data Sources

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

ML ML Data Lakes Machine Learning

How to Manage Unstructured Data in AI and Machine Learning Projects

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

Machine Learning Machine Learning AI AI

Best Data Engineering Tools Every Engineer Should Know

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

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Data Science Current

Understanding ETL Tools as a Data-Centric Organization

How Rocket Companies modernized their data science solution on AWS

Webinars

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Essential data engineering tools for 2023: Empowering for management and analysis

Webinars

Remote Data Science Jobs: 5 High-Demand Roles for Career Growth

Top ETL Tools: Unveiling the Best Solutions for Data Integration

Navigating the Big Data Frontier: A Guide to Efficient Handling

A Guide to Choose the Best Data Science Bootcamp

Data Warehouse vs. Data Lake

How Fivetran and dbt Help With ELT

Popular Data Transformation Tools: Importance and Best Practices

The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Discover the Most Important Fundamentals of Data Engineering

Data platform trinity: Competitive or complementary?

Azure Data Engineer Jobs

How to Effectively Handle Unstructured Data Using AI

How to Version Control Data in ML for Various Data Sources

How to Manage Unstructured Data in AI and Machine Learning Projects

Best Data Engineering Tools Every Engineer Should Know

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