Women in Big Data

OCTOBER 9, 2024

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. This phase ensures quality and consistency using frameworks like Apache Spark or AWS Glue.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

Pickl AI

JULY 25, 2023

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 Visualization: Matplotlib, Seaborn, Tableau, etc. Big Data Technologies: Hadoop, Spark, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Mlearning.ai

MARCH 15, 2023

Experience with cloud platforms like; AWS, AZURE, etc. Experience with visualization tools like; Tableau and Power BI. Knowledge of big data platforms like; Hadoop and Apache Spark. High proficiency in visualization tools like; Tableau, Google Studio, and Power BI.

Data Science 52

Data Science Analytics Analytics Data Analyst 52

Pickl AI

JANUARY 13, 2025

Key Skills Experience with cloud platforms (AWS, Azure). Hadoop , Apache Spark ) is beneficial for handling large datasets effectively. Cloud Computing Skills Familiarize yourself with cloud platforms like AWS , Google Cloud , or Microsoft Azure to manage infrastructure and deploy AI models efficiently.

AI 52

AI AI Machine Learning Machine Learning 52

Pickl AI

DECEMBER 4, 2024

Gain Experience with Big Data Technologies With the rise of Big Data, familiarity with technologies like Hadoop and Spark is essential. Learn to use tools like Tableau, Power BI, or Matplotlib to create compelling visual representations of data. Embrace Cloud Computing Cloud computing is integral to modern Data Science practices.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

Pickl AI

APRIL 2, 2024

Tableau/Power BI: Visualization tools for creating interactive and informative data visualizations. Hadoop/Spark: Frameworks for distributed storage and processing of big data. Cloud Platforms (AWS, Azure, Google Cloud): Infrastructure for scalable and cost-effective data storage and analysis.

Data Science 52

Data Science Machine Learning Machine Learning Predictive Analytics 52

ODSC - Open Data Science

JANUARY 7, 2025

From development environments like Jupyter Notebooks to robust cloud-hosted solutions such as AWS SageMaker, proficiency in these systems is critical. Hadoop, though less common in new projects, is still crucial for batch processing and distributed storage in large-scale environments.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

Alation

FEBRUARY 20, 2020

Alation catalogs and crawls all of your data assets, whether it is in a traditional relational data set (MySQL, Oracle, etc), a SQL on Hadoop system (Presto, SparkSQL,etc), a BI visualization or something in a file system, such as HDFS or AWS S3.

SQL 52

SQL Hadoop Analytics Analytics 52

Pickl AI

FEBRUARY 23, 2025

Best Big Data Tools Popular tools such as Apache Hadoop, Apache Spark, Apache Kafka, and Apache Storm enable businesses to store, process, and analyse data efficiently. Key Features : Scalability : Hadoop can handle petabytes of data by adding more nodes to the cluster. Use Cases : Yahoo!

Big Data 52

Big Data Big Data Apache Hadoop Apache Kafka 52

Pickl AI

MARCH 19, 2025

Tableau Tableau is a popular data visualization tool that enables users to create interactive dashboards and reports. Apache Hive Apache Hive is a data warehouse tool that allows users to query and analyse large datasets stored in Hadoop. Hadoop : An open-source framework for processing Big Data across multiple servers.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52