Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Pickl AI

JULY 25, 2024

In this blog, we’ll delve into the intricacies of data ingestion, exploring its challenges, best practices, and the tools that can help you harness the full potential of your data. Batch Processing In this method, data is collected over a period and then processed in groups or batches.

Apache Kafka 52

Apache Kafka Data Lakes Data Warehouse Data Quality 52

Pickl AI

AUGUST 9, 2024

Data Warehousing Solutions Tools like Amazon Redshift, Google BigQuery, and Snowflake enable organisations to store and analyse large volumes of data efficiently. Students should learn about the architecture of data warehouses and how they differ from traditional databases.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Pickl AI

SEPTEMBER 18, 2024

This blog explores how Netflix applies Big Data across its business operations, focusing on its infrastructure, content strategies, customer engagement, operational efficiency, marketing insights, security measures, and future challenges. The architecture is divided into two main categories: data at rest and data in motion.

Big Data 52

Big Data Big Data Apache Kafka Big Data Analytics 52

Mlearning.ai

MAY 16, 2023

It is used to extract data from various sources, transform the data to fit a specific data model or schema, and then load the transformed data into a target system such as a data warehouse or a database. In the extraction phase, the data is collected from various sources and brought into a staging area.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 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 Engineer 91

Pickl AI

JULY 29, 2024

Log Analysis These are well-suited for analysing log data from various sources, such as web servers, application logs, and sensor data, to gain insights into user behaviour and system performance. Organisations that require low-latency data analysis may find Hadoop insufficient for their needs.

Hadoop 52

Hadoop Clustering 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. Credits can be purchased for 14 cents per minute.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

phData

SEPTEMBER 27, 2024

Looking to build a machine-learning model for churn prediction? The atomic data provides a perfect input, capturing the full richness of customer behavior over time. Technologies like Apache Kafka, often used in modern CDPs, use log-based approaches to stream customer events between systems in real-time.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52