Pickl AI

AUGUST 9, 2024

Velocity It indicates the speed at which data is generated and processed, necessitating real-time analytics capabilities. Businesses need to analyse data as it streams in to make timely decisions. This diversity requires flexible data processing and storage solutions. Once data is collected, it needs to be stored efficiently.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

JULY 8, 2024

Organisations leverage diverse methods to gather data, including: Direct Data Capture: Real-time collection from sensors, devices, or web services. Database Extraction: Retrieval from structured databases using query languages like SQL. Data Warehouses : Centralised repositories optimised for analytics and reporting.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

Dataconomy

FEBRUARY 23, 2023

Collecting, storing, and processing large datasets Data engineers are also responsible for collecting, storing, and processing large volumes of data. This involves working with various data storage technologies, such as databases and data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.

Big Data 91

Big Data Big Data Data Engineering Data Engineering 91

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. Data Migration NiFi facilitates the migration of data between systems, whether on-premises or in the cloud.

ETL 52

ETL Data Lakes Big Data Big Data 52

The MLOps Blog

MARCH 15, 2023

Data Processing : You need to save the processed data through computations such as aggregation, filtering and sorting. Data Storage : To store this processed data to retrieve it over time – be it a data warehouse or a data lake. Relational database connectors are available.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

Pickl AI

JULY 29, 2024

Limited Support for Real-Time Processing While Hadoop excels at batch processing, it is not inherently designed for real-time data processing. Organisations that require low-latency data analysis may find Hadoop insufficient for their needs.

Hadoop 52

Hadoop Clustering Big Data Big Data 52

phData

SEPTEMBER 27, 2024

. “ This sounds great in theory, but how does it work in practice with customer data or something like a ‘composable CDP’? Well, implementing transitional modeling does require a shift in how we think about and work with customer data. It often involves specialized databases designed to handle this kind of atomic, temporal data.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

Pickl AI

MARCH 19, 2025

Python, SQL, and Apache Spark are essential for data engineering workflows. Real-time data processing with Apache Kafka enables faster decision-making. offers Data Science courses covering essential data tools with a job guarantee. What Does a Data Engineer Do?

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 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. Real-Time Data Analysis: Connects seamlessly with various databases for live analysis.

Big Data 52

Big Data Big Data Apache Hadoop Apache Kafka 52