Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Databases and SQL : Managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Smart Data Collective

JULY 20, 2021

These software tools rely on sophisticated big data algorithms and allow companies to boost their sales, business productivity and customer retention. This tool is designed to connect various data sources, enterprise applications and perform analytics and ETL processes. With this tool, data transfer is faster and dynamic.

Big Data 142

Big Data Big Data ETL Analytics 142

Women in Big Data

NOVEMBER 27, 2024

Evaluate integration capabilities with existing data sources and Extract Transform and Load (ETL) tools. Its PostgreSQL foundation ensures compatibility with most SQL clients. Strengths : Real-time analytics, built-in machine learning capabilities, and fast querying with standard SQL.

Data Warehouse 59

Data Warehouse Big Data Big Data Azure 59

Applied Data Science

AUGUST 2, 2021

The most common data science languages are Python and R   —  SQL is also a must have skill for acquiring and manipulating data. They build production-ready systems using best-practice containerisation technologies, ETL tools and APIs. The Data Engineer Not everyone working on a data science project is a data scientist.

Data Science 130

Data Science Data Scientist ML ML 130

AWS Machine Learning Blog

JANUARY 17, 2024

To obtain such insights, the incoming raw data goes through an extract, transform, and load (ETL) process to identify activities or engagements from the continuous stream of device location pings. As part of the initial ETL, this raw data can be loaded onto tables using AWS Glue.

Clustering 133

Clustering AWS ML ML 133

AWS Machine Learning Blog

OCTOBER 9, 2024

Using Amazon CloudWatch for anomaly detection Amazon CloudWatch supports creating anomaly detectors on specific Amazon CloudWatch Log Groups by applying statistical and ML algorithms to CloudWatch metrics. To use this feature, you can write rules or analyzers and then turn on anomaly detection in AWS Glue ETL.

AWS 94

AWS ML ML Data Quality 94

The MLOps Blog

SEPTEMBER 7, 2023

From writing code for doing exploratory analysis, experimentation code for modeling, ETLs for creating training datasets, Airflow (or similar) code to generate DAGs, REST APIs, streaming jobs, monitoring jobs, etc. Implementing these practices can enhance the efficiency and consistency of ETL workflows.

Machine Learning 52

Machine Learning Machine Learning ETL ML 52

IBM Journey to AI blog

SEPTEMBER 25, 2023

This blog takes you on a journey into the world of Uber’s analytics and the critical role that Presto, the open source SQL query engine, plays in driving their success. This allowed them to focus on SQL-based query optimization to the nth degree. What is Presto?

Data Lakes 102

Data Lakes Analytics Analytics Clustering 102

Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. With expertise in programming languages like Python , Java , SQL, and knowledge of big data technologies like Hadoop and Spark, data engineers optimize pipelines for data scientists and analysts to access valuable insights efficiently.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Mlearning.ai

APRIL 24, 2023

One can only train and mange so many algorithms/commands with one computer, thus it is attractive to use a service cloud platform with more computers, storage, and deployment options. run an SQL query that creates an empty table with the column order that you wish and then associate this table with your blobstorage data in Data Factory.

Azure 52

Azure SQL Database Python 52

Pickl AI

JULY 3, 2023

Here are steps you can follow to pursue a career as a BI Developer: Acquire a solid foundation in data and analytics: Start by building a strong understanding of data concepts, relational databases, SQL (Structured Query Language), and data modeling.

Business Intelligence 52

Business Intelligence Business Intelligence SQL Data Visualization 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. SQL excels with big data and statistics, making it important in order to query databases.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

Pickl AI

APRIL 6, 2023

Having a solid understanding of ML principles and practical knowledge of statistics, algorithms, and mathematics. Hands-on experience working with SQLDW and SQL-DB. Answer : Polybase helps optimize data ingestion into PDW and supports T-SQL. Sound knowledge of relational databases or NoSQL databases like Cassandra.

Azure 52

Azure Data Engineering Data Engineer Data Engineering 52

Dataconomy

JULY 28, 2023

This unstructured nature poses challenges for direct analysis, as sentiments cannot be easily interpreted by traditional machine learning algorithms without proper preprocessing. Text data is often unstructured, making it challenging to directly apply machine learning algorithms for sentiment analysis.

Power BI 91

Power BI Data Preparation Exploratory Data Analysis Machine Learning 91

Pickl AI

FEBRUARY 4, 2024

Predictive Analytics: Leverage machine learning algorithms for accurate predictions. Unlike SQL, Alteryx offers a visually intuitive approach, allowing users to focus on analysis without being encumbered by technical intricacies. Is Alteryx an ETL tool? Yes, Alteryx is an ETL (Extract, Transform, Load) tool.

Data Preparation 52

Data Preparation Tableau Data Visualization SQL 52

Pickl AI

APRIL 26, 2024

It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques. Key Takeaways SQL Mastery: Understand SQL’s importance, join tables, and distinguish between SELECT and SELECT DISTINCT. How do you join tables in SQL?

Data Analyst 52

Data Analyst Data Analysis Data Analysis Machine Learning 52

Pickl AI

SEPTEMBER 19, 2024

It also supports ETL (Extract, Transform, Load) processes, making data warehousing and analytics essential. Spark SQL Spark SQL is a module that works with structured and semi-structured data. It allows users to run SQL queries, read data from different sources, and seamlessly integrate with Spark’s core capabilities.

Hadoop 52

Hadoop Big Data Big Data Clustering 52

phData

FEBRUARY 7, 2024

Snowpark is the set of libraries and runtimes in Snowflake that securely deploy and process non-SQL code, including Python, Java, and Scala. DataFrames are able to be created from tables, views, streams, and stages, from the results of a SQL query, or from hardcoded values. What is Snowpark? filter(col("id") == 1).select(col("name"),

Python 52

Python ML ML SQL 52

The MLOps Blog

MARCH 15, 2023

Enables users to trigger their custom transformations via SQL and dbt. The logical flow of running upstream and downstream tasks is decided using an algorithm commonly known as a Directed Acyclic Graph (DAG). Relational database connectors such as Teradata, Oracle, and Microsoft SQL servers are available.

Data Pipeline 59

Data Pipeline ETL SQL Data Quality 59

DataSeries

AUGUST 15, 2024

Knowledge of Core Data Engineering Concepts Ensure one possess a strong foundation in core data engineering concepts, which include data structures, algorithms, database management systems, data modeling , data warehousing , ETL (Extract, Transform, Load) processes, and distributed computing frameworks (e.g., Hadoop, Spark).

Data Engineering 40

Data Engineering Data Engineer Data Engineering Data Engineering 40

DataRobot

JUNE 4, 2021

New algorithms are constantly being added to the platform, from classic linear regression to adaptive neural networks, using an intelligent search to automatically configure the architecture. The process is simple, and if you have a Snowflake account, getting data from the Snowflake Data Marketplace involves only a few clicks.

Machine Learning 52

Machine Learning Machine Learning Algorithm ETL 52

Pickl AI

JANUARY 28, 2025

This involves several key processes: Extract, Transform, Load (ETL): The ETL process extracts data from different sources, transforms it into a suitable format by cleaning and enriching it, and then loads it into a data warehouse or data lake. What Are Some Common Tools Used in Business Intelligence Architecture?

Business Intelligence 52

Business Intelligence Business Intelligence ETL Data Lakes 52

phData

SEPTEMBER 4, 2024

ThoughtSpot is a cloud-based AI-powered analytics platform that uses natural language processing (NLP) or natural language query (NLQ) to quickly query results and generate visualizations without the user needing to know any SQL or table relations. Suppose your business requires more robust capabilities across your technology stack.

Analytics 52

Analytics Analytics SQL ETL 52

Pickl AI

JULY 8, 2024

Database Extraction: Retrieval from structured databases using query languages like SQL. This step often involves: ETL Processes: Extracting, transforming, and loading data into a target system. Read More: Top ETL Tools: Unveiling the Best Solutions for Data Integration.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

Pickl AI

AUGUST 9, 2024

Understanding the differences between SQL and NoSQL databases is crucial for students. Understanding ETL (Extract, Transform, Load) processes is vital for students. Machine Learning Algorithms Basic understanding of Machine Learning concepts and algorithm s, including supervised and unsupervised learning techniques.

Big Data 52

Big Data Big Data Big Data Analytics Big Data Analytics 52

phData

SEPTEMBER 19, 2023

There are tools designed specifically to analyze your data lake files, determine the schema, and allow for SQL statements to be run directly off this data. Through a combination of AWS Glue and AWS Athena, a user can scan their data lake, dynamically creating schema and tables, allowing for SQL queries directly on files stored in Amazon S3.

Data Lakes 52

Data Lakes Data Modeling Data Models Data Warehouse 52

ODSC - Open Data Science

DECEMBER 9, 2024

Switching contexts across tools like Pandas, SciKit-Learn, SQL databases, and visualization engines creates cognitive burden. Were talking automated data cleaning, ETL pipeline generation, feature selection for models, hyperparameter tuningremoving grunt work to free up analyst time/energy for higher thinking.

Data Science 40

Data Science Machine Learning Machine Learning Python 40

DagsHub

APRIL 7, 2024

Image generated with Midjourney In today’s fast-paced world of data science, building impactful machine learning models relies on much more than selecting the best algorithm for the job. Thanks to its various operators, it is integrated with Python, Spark, Bash, SQL, and more.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

DagsHub

OCTOBER 23, 2024

Here’s the structured equivalent of this same data in tabular form: With structured data, you can use query languages like SQL to extract and interpret information. is similar to the traditional Extract, Transform, Load (ETL) process. This text has a lot of information, but it is not structured. Unstructured.io

Machine Learning 52

Machine Learning Machine Learning AI AI 52

phData

OCTOBER 28, 2024

Modern low-code/no-code ETL tools allow data engineers and analysts to build pipelines seamlessly using a drag-and-drop and configure approach with minimal coding. One such option is the availability of Python Components in Matillion ETL, which allows us to run Python code inside the Matillion instance.

Python 52

Python ETL AWS Database 52

The MLOps Blog

AUGUST 3, 2023

At a high level, we are trying to make machine learning initiatives more human capital efficient by enabling teams to more easily get to production and maintain their model pipelines, ETLs, or workflows. Jeff Magnusson has a pretty famous post about engineers shouldn’t write ETL.

ML 52

ML ML Data Scientist Machine Learning 52

phData

SEPTEMBER 27, 2024

Instead of simple SQL queries, we often need to use more complex temporal query languages or rely on derived views for simpler querying. In traditional ETL (Extract, Transform, Load) processes in CDPs, staging areas were often temporary holding pens for data. It also requires a shift in how we query our customer data.

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52