ODSC - Open Data Science

SEPTEMBER 27, 2023

This allows data scientists, analysts, and other stakeholders to perform exploratory analyses and derive insights without prior knowledge of the data structure. This is particularly advantageous when dealing with exponentially growing data volumes. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Smart Data Collective

MAY 20, 2019

By 2020, over 40 percent of all data science tasks will be automated. For frameworks and languages, there’s SAS, Python, R, Apache Hadoop and many others. The popular tools, on the other hand, include Power BI, ETL, IBM Db2, and Teradata. Machine Learning Experience is a Must.

Analytics 111

Analytics Analytics Data Analyst Machine Learning 111

Pickl AI

NOVEMBER 4, 2024

Additionally, Data Engineers implement quality checks, monitor performance, and optimise systems to handle large volumes of data efficiently. Differences Between Data Engineering and Data Science While Data Engineering and Data Science are closely related, they focus on different aspects of data.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

NOVEMBER 15, 2023

What Is a Data Warehouse? On the other hand, a Data Warehouse is a structured storage system designed for efficient querying and analysis. It involves the extraction, transformation, and loading (ETL) process to organize data for business intelligence purposes. It often serves as a source for Data Warehouses.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

Pickl AI

AUGUST 9, 2024

Big Data Technologies and Tools A comprehensive syllabus should introduce students to the key technologies and tools used in Big Data analytics. Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Mlearning.ai

JULY 10, 2023

In my 7 years of Data Science journey, I’ve been exposed to a number of different databases including but not limited to Oracle Database, MS SQL, MySQL, EDW, and Apache Hadoop. A lot of you who are already in the data science field must be familiar with BigQuery and its advantages.

SQL 52

SQL Database Apache Hadoop Data Science 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 8, 2024

Tools such as Python’s Pandas library, Apache Spark, or specialised data cleaning software streamline these processes, ensuring data integrity before further transformation. Step 3: Data Transformation Data transformation focuses on converting cleaned data into a format suitable for analysis and storage.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

Dataconomy

FEBRUARY 23, 2023

Creating data pipelines and workflows Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently. By creating efficient data pipelines and workflows, data engineers enable organizations to make data-driven decisions quickly and accurately.

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

The MLOps Blog

JANUARY 23, 2023

With lakeFS it is possible to test ETLs on top of production data, in isolation, without copying anything. Also, lakeFS can be used for data management, ETL testing, reproducibility for experiments, and CI/CD for data to prevent future failures.

ML 52

ML ML Data Lakes Machine Learning 52

Alation

FEBRUARY 20, 2020

With the year coming to a close, many look back at the headlines that made major waves in technology and big data – from Spark to Hadoop to trends in data science – the list could go on and on. The infrastructure to support the ‘logical data warehouse’ is deployed already from Presto to Teradata UDA, etc.

Data Warehouse 52

Data Warehouse Hadoop Data Science ETL 52

IBM Journey to AI blog

SEPTEMBER 25, 2023

Uber chose Presto for the flexibility it provides with compute separated from data storage. As a result, they continue to expand their use cases to include ETL, data science , data exploration, online analytical processing (OLAP), data lake analytics and federated queries.

Data Lakes 102

Data Lakes Analytics Analytics Clustering 102

Pickl AI

MARCH 19, 2025

Learning these tools is crucial for building scalable data pipelines. offers Data Science courses covering these tools with a job guarantee for career growth. Introduction Imagine a world where data is a messy jungle, and we need smart tools to turn it into useful insights.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52