Analytics Vidhya

MAY 5, 2022

This article was published as a part of the Data Science Blogathon. Introduction With a huge increment in data velocity, value, and veracity, the volume of data is growing exponentially with time. This outgrows the storage limit and enhances the demand for storing the data across a network of machines.

Data Science 270

Data Science Analytics Analytics Apache Hadoop 270

Pickl AI

JANUARY 12, 2025

Unify Data Sources Collect data from multiple systems into one cohesive dataset. To confirm seamless integration, you can use tools like Apache Hadoop, Microsoft Power BI, or Snowflake to process structured data and Elasticsearch or AWS for unstructured data.

Artificial Intelligence 52

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Pickl AI

MARCH 10, 2023

However, despite being a lucrative career option, Data Scientists face several challenges occasionally. The following blog will discuss the familiar Data Science challenges professionals face daily. It contains data clustering, classification, anomaly detection and time-series forecasting.

Data Scientist 52

Data Scientist Data Science Apache Hadoop Machine Learning 52

Pickl AI

MAY 29, 2024

Skills in data manipulation and cleaning are necessary to prepare data for analysis. Data Scientists frequently use tools like pandas in Python and dplyr in R to transform and clean data sets, ensuring accuracy in subsequent analyses. Data Visualisation Visualisation of data is a critical skill.

Data Scientist 52

Data Scientist Data Science Machine Learning Machine Learning 52

Pickl AI

MAY 15, 2023

Distributed processing is commonly in use for big data analytics, distributed databases and distributed computing frameworks like Hadoop and Spark. Multi-processing: it is the type of data processing in which two or more processors tend to work on the same dataset at the same time. The Data Science courses provided by Pickl.AI

Machine Learning 52

Machine Learning Machine Learning Data Analysis Data Analysis 52

DagsHub

OCTOBER 23, 2024

Now that you know why it is important to manage unstructured data correctly and what problems it can cause, let's examine a typical project workflow for managing unstructured data. They enable flexible data storage and retrieval for diverse use cases, making them highly scalable for big data applications.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 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