Women in Big Data

OCTOBER 9, 2024

Components of a Big Data Pipeline Data Sources (Collection): Data originates from various sources, such as databases, APIs, and log files. Examples include transactional databases, social media feeds, and IoT sensors. 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

DECEMBER 2, 2024

This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). In-Memory Databases: Databases such as Redis store data in memory for lightning-fast access and processing speeds. Variety Variety indicates the different types of data being generated.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 25, 2024

This includes structured data (like databases), semi-structured data (like XML files), and unstructured data (like text documents and videos). In-Memory Databases: Databases such as Redis store data in memory for lightning-fast access and processing speeds. Variety Variety indicates the different types of data being generated.

Big Data 52

Big Data Big Data Data Lakes Apache Hadoop 52

Pickl AI

NOVEMBER 4, 2024

They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes. Data Modelling Data modelling is creating a visual representation of a system or database. Physical Models: These models specify how data will be physically stored in databases.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

DagsHub

OCTOBER 23, 2024

Data can come from different sources, such as databases or directly from users, with additional sources, including platforms like GitHub, Notion, or S3 buckets. Vector Databases Vector databases help store unstructured data by storing the actual data and its vector representation. mp4,webm, etc.), and audio files (.wav,mp3,acc,

Machine Learning 52

Machine Learning Machine Learning AI AI 52

Pickl AI

JULY 30, 2024

Below are some prominent use cases for Apache NiFi: Data Ingestion from Diverse Sources NiFi excels at collecting data from various sources, including log files, sensors, databases, and APIs. It can connect to various database s, file systems, and cloud storage solutions, enabling seamless data transfer without significant downtime.

ETL 52

ETL Data Lakes Big Data Big Data 52

Pickl AI

JULY 29, 2024

Setting up a Hadoop cluster involves the following steps: Hardware Selection Choose the appropriate hardware for the master node and worker nodes, considering factors such as CPU, memory, storage, and network bandwidth. Apache Hadoop, Cloudera, Hortonworks). Download and extract the Apache Hadoop distribution on all nodes.

Hadoop 52

Hadoop Clustering Big Data Big Data 52