Smart Data Collective

JULY 9, 2023

ETL (Extract, Transform, Load) is a crucial process in the world of data analytics and business intelligence. In this article, we will explore the significance of ETL and how it plays a vital role in enabling effective decision making within businesses. What is ETL? Let’s break down each step: 1.

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Data Science Blog

SEPTEMBER 19, 2023

In the contemporary age of Big Data, Data Warehouse Systems and Data Science Analytics Infrastructures have become an essential component for organizations to store, analyze, and make data-driven decisions. So why using IaC for Cloud Data Infrastructures?

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Data Warehouse Azure SQL Database 259

Dataconomy

FEBRUARY 23, 2023

Data engineers play a crucial role in managing and processing big data. They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. Implementing data security measures Data security is a critical aspect of data engineering.

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

Women in Big Data

OCTOBER 9, 2024

With the explosive growth of big data over the past decade and the daily surge in data volumes, it’s essential to have a resilient system to manage the vast influx of information without failures. The success of any data initiative hinges on the robustness and flexibility of its big data pipeline.

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Big Data Big Data Apache Kafka Data Pipeline 59

Precisely

JUNE 27, 2024

The efficiency of ETL integration can make or break the rest of your data management workflow. Want to get the most from your ETL processes? Keep reading for high-performance ETL best practices. 8 ETL best practices For optimum integration results, here’s eight of our best tips.

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Precisely

JANUARY 9, 2025

The magic of the data warehouse was figuring out how to get data out of these transactional systems and reorganize it in a structured way optimized for analysis and reporting. Then came Big Data and Hadoop! The big data boom was born, and Hadoop was its poster child.

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Data Warehouse Hadoop Data Lakes Data Governance 119

Flipboard

NOVEMBER 24, 2023

The SnapLogic Intelligent Integration Platform (IIP) enables organizations to realize enterprise-wide automation by connecting their entire ecosystem of applications, databases, big data, machines and devices, APIs, and more with pre-built, intelligent connectors called Snaps.

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Database AWS ETL SQL 158

Women in Big Data

NOVEMBER 27, 2024

A data warehouse is a centralized repository designed to store and manage vast amounts of structured and semi-structured data from multiple sources, facilitating efficient reporting and analysis. Begin by determining your data volume, variety, and the performance expectations for querying and reporting.

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Data Warehouse Big Data Big Data Azure 59

Pickl AI

OCTOBER 6, 2024

Summary: This blog explores the key differences between ETL and ELT, detailing their processes, advantages, and disadvantages. Understanding these methods helps organizations optimize their data workflows for better decision-making. What is ETL? ETL stands for Extract, Transform, and Load.

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Smart Data Collective

AUGUST 4, 2022

A growing number of businesses are relying on big data technology to improve productivity and address some of their most pressing challenges. Global companies are projected to spend over $297 billion on big data by 2030. Data technology has proven to be remarkably helpful for many businesses. Problem Statement.

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Smart Data Collective

OCTOBER 17, 2022

Big data is shaping our world in countless ways. Data powers everything we do. Exactly why, the systems have to ensure adequate, accurate and most importantly, consistent data flow between different systems. Its underlying Singer framework allows the data teams to customize the pipeline with ease.

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Data Pipeline Data Warehouse ETL Data Lakes 145

Pickl AI

AUGUST 9, 2024

Summary: A comprehensive Big Data syllabus encompasses foundational concepts, essential technologies, data collection and storage methods, processing and analysis techniques, and visualisation strategies. Fundamentals of Big Data Understanding the fundamentals of Big Data is crucial for anyone entering this field.

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Flipboard

DECEMBER 11, 2024

With SageMaker Unified Studio notebooks, you can use Python or Spark to interactively explore and visualize data, prepare data for analytics and ML, and train ML models. With the SQL editor, you can query data lakes, databases, data warehouses, and federated data sources. option("multiLine", "true").option("header",

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SQL AWS Data Lakes AI 160

Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Choosing the right ETL tool is crucial for smooth data management.

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ODSC - Open Data Science

SEPTEMBER 27, 2023

In the ever-evolving world of big data, managing vast amounts of information efficiently has become a critical challenge for businesses across the globe. Understanding Data Lakes A data lake is a centralized repository that stores structured, semi-structured, and unstructured data in its raw format.

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Data Lakes Data Warehouse Database Big Data 98

Becoming Human

JANUARY 23, 2023

Let’s understand with an example if we consider web development so there are UI , UX , Database , Networking , and Servers and for implementing all these things we have different-different tools - technologies and frameworks , and when we have done with these things we just called this process as web development.

Data Science 105

Data Science Big Data Big Data Deep Learning 105

How to Learn Machine Learning

APRIL 26, 2025

Each component in this ecosystem is very important in the data-driven decision-making process for an organization. Data Sources and Collection Everything in data science begins with data. Data can be generated from databases, sensors, social media platforms, APIs, logs, and web scraping.

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Tableau

MARCH 30, 2021

we’ve added new connectors to help our customers access more data in Azure than ever before: an Azure SQL Database connector and an Azure Data Lake Storage Gen2 connector. As our customers increasingly adopt the cloud, we continue to make investments that ensure they can access their data anywhere. Azure SQL Database.

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Azure Tableau Data Lakes SQL 102

Towards AI

APRIL 3, 2024

The ORC and Parquet are columnal storage and they are famous in the Big Data world because of their efficient storage. Glue Crawler Setup The next step is setting up a Glue crawler to extract the schema of this file and create a database. Create a new Glue Crawler to discover and catalog your data in S3.

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AWS Database ETL Big Data 105

Flipboard

JUNE 26, 2023

Transform raw insurance data into CSV format acceptable to Neptune Bulk Loader , using an AWS Glue extract, transform, and load (ETL) job. When the data is in CSV format, use an Amazon SageMaker Jupyter notebook to run a PySpark script to load the raw data into Neptune and visualize it in a Jupyter notebook. Choose Next.

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Dataconomy

SEPTEMBER 4, 2023

Big data analytics: Big data analytics is designed to handle massive volumes of data from various sources, including structured and unstructured data. Big data analytics is essential for organizations dealing with large-scale data, such as social media platforms, e-commerce giants, and scientific research.

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Analytics Analytics Big Data Analytics Big Data Analytics 203

Data Science Dojo

JULY 3, 2024

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python.

Data Science 150

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Smart Data Collective

MAY 20, 2019

We’re well past the point of realization that big data and advanced analytics solutions are valuable — just about everyone knows this by now. Big data alone has become a modern staple of nearly every industry from retail to manufacturing, and for good reason.

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AWS Machine Learning Blog

FEBRUARY 2, 2024

In this pattern, the recipe text is converted into embedding vectors using an embedding model, and stored in a vector database. Incoming questions are converted to embeddings, and then the vector database runs a similarity search to find related content. The question and the reference data then go into the prompt for the LLM.

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Smart Data Collective

APRIL 29, 2020

The storage and processing of data through a cloud-based system of applications. Master data management. The techniques for managing organisational data in a standardised approach that minimises inefficiency. Extraction, Transform, Load (ETL). Data transformation.

Data Warehouse 97

Data Warehouse Azure SQL ETL 97

Dataconomy

OCTOBER 17, 2023

Data warehouse architecture The data warehouse architecture is a very critical concept regarding big data. It could be defined as the layout and design of a data warehouse, which at other times could act as a central repository for all organization’s data.

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phData

AUGUST 9, 2023

In short, ELT exemplifies the data strategy required in the era of big data, cloud, and agile analytics. With ELT, we first extract data from source systems, then load the raw data directly into the data warehouse before finally applying transformations natively within the data warehouse.

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phData

OCTOBER 9, 2023

In this blog, we explore best practices and techniques to optimize Snowflake’s performance for data vault modeling , enabling your organizations to achieve efficient data processing, accelerated query performance, and streamlined ETL workflows. However, joining tables using a hash key can take longer than a sequential key.

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Smart Data Collective

NOVEMBER 9, 2020

When you transfer data from one system to another, it is called data migration. Data migration involves radically changing the storage process, database or application. It may also entail the transfer of data between operating systems or databases. The process can involve transferring between servers.

Database 108

Database ETL Big Data Big Data 108

Pickl AI

JULY 30, 2024

Its architecture includes FlowFiles, repositories, and processors, enabling efficient data processing and transformation. With a user-friendly interface and robust features, NiFi simplifies complex data workflows and enhances real-time data integration. Its visual interface allows users to design complex ETL workflows with ease.

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ETL Data Lakes Big Data Big Data 52

Pickl AI

JUNE 11, 2024

Summary: A data warehouse is a central information hub that stores and organizes vast amounts of data from different sources within an organization. Unlike operational databases focused on daily tasks, data warehouses are designed for analysis, enabling historical trend exploration and informed decision-making.

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Mlearning.ai

MAY 16, 2023

This article discusses five commonly used architectural design patterns in data engineering and their use cases. ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. Finally, the transformed data is loaded into the target system.

Data Engineering 52

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Precisely

MARCH 9, 2023

Raw Data Data warehouses emerged several decades ago as a means of combining, harmonizing, and preprocessing data in preparation for advanced analytics. A data warehouse implies a certain degree of preprocessing, or at the very least, an organized and well-defined data model. They are malleable.

Data Lakes 59

Data Lakes Data Warehouse Hadoop Big Data 59

AWS Machine Learning Blog

AUGUST 2, 2024

Embeddings generation – An embeddings model is used to encode the semantic information of each chunk into an embeddings vector, which is stored in a vector database, enabling similarity search of user queries. Based on the query embeddings, the relevant documents are retrieved from the embeddings database using similarity search.

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ODSC - Open Data Science

APRIL 3, 2023

Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis. Competence in data quality, databases, and ETL (Extract, Transform, Load) are essential.

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Analytics Analytics Data Analyst Data Science 52

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

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Pickl AI

JULY 6, 2023

Thus, making it easier for analysts and data scientists to leverage their SQL skills for Big Data analysis. Schema-on-Read Unlike traditional databases, Hive follows a schema-on-read approach. It applies the data structure during querying rather than data ingestion. Why Do We Need Hadoop Hive?

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