Precisely

MARCH 9, 2023

As cloud computing platforms make it possible to perform advanced analytics on ever larger and more diverse data sets, new and innovative approaches have emerged for storing, preprocessing, and analyzing information. Hadoop, Snowflake, Databricks and other products have rapidly gained adoption.

Data Lakes 59

Data Lakes Data Warehouse Hadoop Big Data 59

ODSC - Open Data Science

SEPTEMBER 27, 2023

Flexibility and Agility Data lakes provide flexibility, enabling organizations to store diverse data types without worrying about immediate data modeling. This allows data scientists, analysts, and other stakeholders to perform exploratory analyses and derive insights without prior knowledge of the data structure.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Pickl AI

NOVEMBER 4, 2024

They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes. Their work ensures that data flows seamlessly through the organisation, making it easier for Data Scientists and Analysts to access and analyse information.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Mlearning.ai

MAY 16, 2023

There are various architectural design patterns in data engineering that are used to solve different data-related problems. This article discusses five commonly used architectural design patterns in data engineering and their use cases. Finally, the transformed data is loaded into the target system.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 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

Pickl AI

NOVEMBER 15, 2023

Unlike traditional databases, Data Lakes enable storage without the need for a predefined schema, making them highly flexible. Importance of Data Lakes Data Lakes play a pivotal role in modern data analytics, providing a platform for Data Scientists and analysts to extract valuable insights from diverse data sources.

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

Dataconomy

FEBRUARY 23, 2023

They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. This involves working closely with data analysts and data scientists to ensure that data is stored, processed, and analyzed efficiently to derive insights that inform decision-making.

Big Data 91

Big Data Big Data Data Engineering Data Engineering 91

DataSeries

AUGUST 15, 2024

Knowledge of Core Data Engineering Concepts Ensure one possess a strong foundation in core data engineering concepts, which include data structures, algorithms, database management systems, data modeling , data warehousing , ETL (Extract, Transform, Load) processes, and distributed computing frameworks (e.g.,

Data Engineering 40

Data Engineering Data Engineering Data Engineer Data Engineering 40

Pickl AI

APRIL 6, 2023

Data Engineering is one of the most productive job roles today because it imbibes both the skills required for software engineering and programming and advanced analytics needed by Data Scientists. How to Become an Azure Data Engineer? For Azure Data Engineer, there are various skills required.

Azure 52

Azure Data Engineering Data Engineering Data Engineer 52

The MLOps Blog

JANUARY 23, 2023

When it comes to data complexity, it is for sure that in machine learning, we are dealing with much more complex data. First of all, machine learning engineers and data scientists often use data from different data vendors. Some data sets are being corrected by data entry specialists and manual inspectors.

ML 52

ML ML Data Lakes Machine Learning 52

The MLOps Blog

MAY 31, 2023

So, a better database architecture would be to maintain multiple tables where one of the tables maintains the past 3 months history with session-level details, whereas other tables may contain weekly aggregated click, ATC, and order data. Keeping track of which data was used to run an experiment sometimes becomes painful for a Data Scientist.

ML 59

ML ML Algorithm Machine Learning 59

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

Alation

FEBRUARY 20, 2020

Business Intelligence used to require months of effort from BI and ETL teams. More recently, we’ve seen Extract, Transform and Load (ETL) tools like Informatica and IBM Datastage disrupted by self-service data preparation tools. First, there is no easy way to find the data you want to prepare.

ETL 52

ETL Hadoop Tableau Data Scientist 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

Data Science focuses on analysing data to find patterns and make predictions. Data engineering, on the other hand, builds the foundation that makes this analysis possible. Without well-structured data, Data Scientists cannot perform their work efficiently.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52