Remove Business Intelligence Remove Data Preparation Remove Hadoop
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Data lakes vs. data warehouses: Decoding the data storage debate

Data Science Dojo

It can process any type of data, regardless of its variety or magnitude, and save it in its original format. Hadoop systems and data lakes are frequently mentioned together. However, instead of using Hadoop, data lakes are increasingly being constructed using cloud object storage services.

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Data science

Dataconomy

Key disciplines involved in data science Understanding the core disciplines within data science provides a comprehensive perspective on the field’s multifaceted nature. Overview of core disciplines Data science encompasses several key disciplines including data engineering, data preparation, and predictive analytics.

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Emerging Data Science Trends in 2025 You Need to Know

Pickl AI

The Rise of Augmented Analytics Augmented analytics is revolutionizing how data insights are generated by integrating artificial intelligence (AI) and machine learning (ML) into analytics workflows. APA enables businesses to enhance efficiency, reduce costs, and accelerate insights by automating repetitive analytical tasks.

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Data Science Career Paths: Analyst, Scientist, Engineer – What’s Right for You?

How to Learn Machine Learning

Data Storage and Management Once data have been collected from the sources, they must be secured and made accessible. The responsibilities of this phase can be handled with traditional databases (MySQL, PostgreSQL), cloud storage (AWS S3, Google Cloud Storage), and big data frameworks (Hadoop, Apache Spark).

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Navigating Data: Alation + Trifacta

Alation

Business Intelligence used to require months of effort from BI and ETL teams. More recently, we’ve seen Extract, Transform and Load (ETL) tools like Informatica and IBM Datastage disrupted by self-service data preparation tools. First, there is no easy way to find the data you want to prepare.

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10 Best Data Engineering Books [Beginners to Advanced]

Pickl AI

Data Pipeline Orchestration: Managing the end-to-end data flow from data sources to the destination systems, often using tools like Apache Airflow, Apache NiFi, or other workflow management systems. It covers Data Engineering aspects like data preparation, integration, and quality.

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Popular Data Transformation Tools: Importance and Best Practices

Pickl AI

Inconsistent or unstructured data can lead to faulty insights, so transformation helps standardise data, ensuring it aligns with the requirements of Analytics, Machine Learning , or Business Intelligence tools. This makes drawing actionable insights, spotting patterns, and making data-driven decisions easier.