article thumbnail

Here’s Why Automation For Data Lakes Could Be Important

Smart Data Collective

Data Lakes are among the most complex and sophisticated data storage and processing facilities we have available to us today as human beings. Analytics Magazine notes that data lakes are among the most useful tools that an enterprise may have at its disposal when aiming to compete with competitors via innovation.

article thumbnail

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

Data Science Dojo

Data engineering tools offer a range of features and functionalities, including data integration, data transformation, data quality management, workflow orchestration, and data visualization. Essential data engineering tools for 2023 Top 10 data engineering tools to watch out for in 2023 1.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Beyond data: Cloud analytics mastery for business brilliance

Dataconomy

Here are some of the key types of cloud analytics: Descriptive analytics: This type focuses on summarizing historical data to provide insights into what has happened in the past. It helps organizations understand trends, patterns, and anomalies in their data. Ensure that data is clean, consistent, and up-to-date.

Analytics 203
article thumbnail

Getir end-to-end workforce management: Amazon Forecast and AWS Step Functions

AWS Machine Learning Blog

He worked at Turkcell, mainly focused on time series forecasting, data visualization, and network automation. He joined Getir in 2021 and currently works as a Data Science & Analytics Manager with the responsibility of Search, Recommendation, and Growth domains.

AWS 99
article thumbnail

Apply fine-grained data access controls with AWS Lake Formation in Amazon SageMaker Data Wrangler

AWS Machine Learning Blog

You can streamline the process of feature engineering and data preparation with SageMaker Data Wrangler and finish each stage of the data preparation workflow (including data selection, purification, exploration, visualization, and processing at scale) within a single visual interface.

AWS 83
article thumbnail

How Getir reduced model training durations by 90% with Amazon SageMaker and AWS Batch

AWS Machine Learning Blog

He worked at Turkcell, mainly focused on time series forecasting, data visualization, and network automation. He joined Getir in 2021 and currently works as a Data Science & Analytics Manager with the responsibility of Search, Recommendation, and Growth domains.

AWS 94
article thumbnail

5 Best Practices for Extracting, Analyzing, and Visualizing Data

Smart Data Collective

There are several choices to consider, each with its own set of advantages and disadvantages: Data warehouses are used to store data that has been processed for a specific function from one or more sources. Data lakes hold raw data that has not yet been altered to meet a specific purpose.