Women in Big Data

OCTOBER 9, 2024

The success of any data initiative hinges on the robustness and flexibility of its big data pipeline. What is a Data Pipeline? A traditional data pipeline is a structured process that begins with gathering data from various sources and loading it into a data warehouse or data lake.

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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.

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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.

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Pickl AI

SEPTEMBER 18, 2024

It utilises Amazon Web Services (AWS) as its main data lake, processing over 550 billion events daily—equivalent to approximately 1.3 petabytes of data. The architecture is divided into two main categories: data at rest and data in motion. What Technologies Does Netflix Use for Its Big Data Infrastructure?

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Pickl AI

JULY 30, 2024

ETL (Extract, Transform, Load) Processes Apache NiFi can streamline ETL processes by extracting data from multiple sources, transforming it into the desired format, and loading it into target systems such as data warehouses or databases. Its visual interface allows users to design complex ETL workflows with ease.

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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.

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Pickl AI

JULY 8, 2024

NoSQL Databases: Flexible, scalable solutions for unstructured or semi-structured data. Data Warehouses : Centralised repositories optimised for analytics and reporting. Data Lakes : Scalable storage for raw and processed data, supporting diverse data types.

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