Data Science Dojo

JULY 6, 2023

Data engineering tools are software applications or frameworks specifically designed to facilitate the process of managing, processing, and transforming large volumes of data. It integrates seamlessly with other AWS services and supports various data integration and transformation workflows.

Data Engineering 195

Data Engineering Data Engineer Data Engineering Data Engineering 195

AWS Machine Learning Blog

NOVEMBER 20, 2024

Repeat the steps to add another Aurora MySQL data source, called aggregated_sales , for the same database but with the following details in the Sync scope This data source will be used by Amazon Q for answering questions on aggregated sales. Data Engineer at Amazon Ads. He has experience across analytics, big data, and ETL.

Database 113

Database AWS SQL ETL 113

Women in Big Data

NOVEMBER 27, 2024

Optimized for analytical processing, it uses specialized data models to enhance query performance and is often integrated with business intelligence tools, allowing users to create reports and visualizations that inform organizational strategies. Its PostgreSQL foundation ensures compatibility with most SQL clients.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

Flipboard

DECEMBER 11, 2024

Data processing and SQL analytics Analyze, prepare, and integrate data for analytics and AI using Amazon Athena, Amazon EMR, AWS Glue, and Amazon Redshift. Data and AI governance Publish your data products to the catalog with glossaries and metadata forms. The SQL ran on AWS Glue for Spark.

SQL 160

SQL AWS Data Lakes AI 160

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.

ETL 52

ETL Data Warehouse Data Quality Data Lakes 52

How to Learn Machine Learning

APRIL 26, 2025

Data can be generated from databases, sensors, social media platforms, APIs, logs, and web scraping. Data can be in structured (like tables in databases), semi-structured (like XML or JSON), or unstructured (like text, audio, and images) form.

Data Science 52

Data Science Data Analyst Data Scientist Machine Learning 52

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.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

Flipboard

NOVEMBER 24, 2023

This use case highlights how large language models (LLMs) are able to become a translator between human languages (English, Spanish, Arabic, and more) and machine interpretable languages (Python, Java, Scala, SQL, and so on) along with sophisticated internal reasoning. Room for improvement!

Database 159

Database AWS ETL SQL 159

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.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

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.

Azure 102

Azure Tableau Data Lakes SQL 102

Pickl AI

JANUARY 27, 2025

Summary: HDFS in Big Data uses distributed storage and replication to manage massive datasets efficiently. By co-locating data and computations, HDFS delivers high throughput, enabling advanced analytics and driving data-driven insights across various industries. It fosters reliability. between 2024 and 2030.

Hadoop 52

Hadoop Big Data Big Data Clustering 52

phData

JULY 10, 2024

Sigma Computing , a cloud-based analytics platform, helps data analysts and business professionals maximize their data with collaborative and scalable analytics. One of Sigma’s key features is its support for custom SQL queries and CSV file uploads. These tools allow users to handle more advanced data tasks and analyses.

SQL 52

SQL Data Warehouse Analytics Analytics 52

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.

AWS 123

AWS ML ML ETL 123

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

Data Science Machine Learning Machine Learning Data Visualization 150

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 SQL Azure ETL 97

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. But it’s not the only skill necessary to thrive.

Analytics 111

Analytics Analytics Data Analyst Machine Learning 111

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. This can make it nearly impossible to “handwrite” these SQL queries.

ETL 69

ETL Clustering Data Warehouse SQL 69

Towards AI

APRIL 3, 2024

So if you are familiar with the Standard SQL queries, you are good to go!! The ORC and Parquet are columnal storage and they are famous in the Big Data world because of their efficient storage. Create a new Glue Crawler to discover and catalog your data in S3. Create a Glue Job to perform ETL operations on your data.

AWS 105

AWS Database ETL Big Data 105

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.

ETL 52

ETL Data Warehouse Cloud Data Big Data 52

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. Unlike traditional data warehouses or relational databases, data lakes accept data from a variety of sources, without the need for prior data transformation or schema definition.

Data Lakes 98

Data Lakes Data Warehouse Database Big Data 98

Pickl AI

JULY 6, 2023

Hive is a data warehousing infrastructure built on top of Hadoop. It has the following features: It facilitates querying, summarizing, and analyzing large datasets Hadoop also provides a SQL-like language called HiveQL Hive allows users to write queries to extract valuable insights from structured and semi-structured data stored in Hadoop.

Hadoop 52

Hadoop SQL Big Data Big Data 52

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.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

Pickl AI

SEPTEMBER 19, 2024

It discusses performance, use cases, and cost, helping you choose the best framework for your big data needs. Introduction Apache Spark and Hadoop are potent frameworks for big data processing and distributed computing. Apache Spark is an open-source, unified analytics engine for large-scale data processing.

Hadoop 52

Hadoop Big Data Big Data Clustering 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.

Data Engineering 52

Data Engineering Data Engineering Data Engineering Data Engineer 52

Tableau

MARCH 29, 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.

Azure 52

Azure Tableau Data Lakes SQL 52

Pickl AI

APRIL 6, 2023

Data Engineering is one of the most productive job roles today because it imbibes both the skills required for software engineering and programming and advanced analytics needed by Data Scientists. How to Become an Azure Data Engineer? Having experience using at least one end-to-end Azure data lake project.

Azure 52

Azure Data Engineering Data Engineering Data Engineering 52

Alation

JANUARY 17, 2023

A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Reverse ETL tools. Data orchestration tools. These tools are used to manage big data, which is defined as data that is too large or complex to be processed by traditional means.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

ODSC - Open Data Science

SEPTEMBER 29, 2023

But, the amount of data companies must manage is growing at a staggering rate. Research analyst firm Statista forecasts global data creation will hit 180 zettabytes by 2025. In our discussion, we cover the genesis of the HPCC Systems data lake platform and what makes it different from other big data solutions currently available.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

Pickl AI

NOVEMBER 4, 2024

Introduction Data Engineering is the backbone of the data-driven world, transforming raw data into actionable insights. As organisations increasingly rely on data to drive decision-making, understanding the fundamentals of Data Engineering becomes essential. ETL is vital for ensuring data quality and integrity.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Mlearning.ai

JULY 10, 2023

In my 7 years of Data Science journey, I’ve been exposed to a number of different databases including but not limited to Oracle Database, MS SQL, MySQL, EDW, and Apache Hadoop. link] Tables The table in GCP BigQuery is a collection of rows and columns that can store and manage massive amounts of data.

SQL 52

SQL Database Apache Hadoop Data Science 52

Pickl AI

NOVEMBER 15, 2023

Data scientists can explore, experiment, and derive valuable insights without the constraints of a predefined structure. This capability empowers organizations to uncover hidden patterns, trends, and correlations in their data, leading to more informed decision-making. What Is a Data Warehouse?

Data Lakes 52

Data Lakes Data Warehouse Database ETL 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

An example direct acyclic graph (DAG) might automate data ingestion, processing, model training, and deployment tasks, ensuring that each step is run in the correct order and at the right time. Though it’s worth mentioning that Airflow isn’t used at runtime as is usual for extract, transform, and load (ETL) tasks.

AWS 122

AWS Machine Learning Machine Learning ML 122

Pickl AI

JANUARY 28, 2025

Data Integration Once data is collected from various sources, it needs to be integrated into a cohesive format. Data Quality Management : Ensures that the integrated data is accurate, consistent, and reliable for analysis. They store structured data in a format that facilitates easy access and analysis.

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

The MLOps Blog

MARCH 15, 2023

Uses secure protocols for data security. Enables users to trigger their custom transformations via SQL and dbt. Ensures data protection and leaks by ensuring best practices for data storage. Best data pipeline tools: Talend | Source Categorization Open Source Batch data processing Pros Apache license makes it free to use.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

AWS Machine Learning Blog

AUGUST 21, 2023

Our customers wanted the ability to connect to Amazon EMR to run ad hoc SQL queries on Hive or Presto to query data in the internal metastore or external metastore (such as the AWS Glue Data Catalog ), and prepare data within a few clicks. You can also query, explore, and visualize data from Amazon EMR.

AWS 98

AWS Data Lakes Clustering Data Preparation 98

Pickl AI

APRIL 26, 2024

This comprehensive blog outlines vital aspects of Data Analyst interviews, offering insights into technical, behavioural, and industry-specific questions. It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques.

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52