phData

AUGUST 6, 2024

As today’s world keeps progressing towards data-driven decisions, organizations must have quality data created from efficient and effective data pipelines. For customers in Snowflake, Snowpark is a powerful tool for building these effective and scalable data pipelines.

Data Pipeline 52

Data Pipeline Python Data Engineering Data Engineer 52

Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

phData

AUGUST 17, 2023

In the rapidly evolving landscape of data engineering, Snowflake Data Cloud has emerged as a leading cloud-based data warehousing solution, providing powerful capabilities for storing, processing, and analyzing vast amounts of data. What are Orchestration Tools?

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

AWS Machine Learning Blog

AUGUST 8, 2024

Managing and retrieving the right information can be complex, especially for data analysts working with large data lakes and complex SQL queries. This tool converts questions from data analysts asked in natural language (such as “Which table contains customer address information?”)

SQL 114

SQL Data Lakes Data Analyst AWS 114

Data Science Dojo

SEPTEMBER 11, 2024

These experiences facilitate professionals from ingesting data from different sources into a unified environment and pipelining the ingestion, transformation, and processing of data to developing predictive models and analyzing the data by visualization in interactive BI reports.

Power BI 195

Power BI Data Pipeline Data Warehouse Data Engineering 195

DataSeries

AUGUST 15, 2024

Enrich data engineering skills by building problem-solving ability with real-world projects, teaming with peers, participating in coding challenges, and more. Globally several organizations are hiring data engineers to extract, process and analyze information, which is available in the vast volumes of data sets.

Data Engineering 40

Data Engineering Data Engineering Data Engineer Data Engineering 40

Analytics Vidhya

SEPTEMBER 22, 2022

This article was published as a part of the Data Science Blogathon. Introduction When creating data pipelines, Software Engineers and Data Engineers frequently work with databases using Database Management Systems like PostgreSQL.

Database 307

Database Data Pipeline Data Engineering Data Engineering 307

Pickl AI

AUGUST 1, 2023

Aspiring and experienced Data Engineers alike can benefit from a curated list of books covering essential concepts and practical techniques. These 10 Best Data Engineering Books for beginners encompass a range of topics, from foundational principles to advanced data processing methods. What is Data Engineering?

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

ODSC - Open Data Science

JANUARY 18, 2024

Data engineering is a rapidly growing field, and there is a high demand for skilled data engineers. If you are a data scientist, you may be wondering if you can transition into data engineering. In this blog post, we will discuss how you can become a data engineer if you are a data scientist.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Data Science Dojo

JANUARY 27, 2023

Data Science Dojo is offering Airbyte for FREE on Azure Marketplace packaged with a pre-configured web environment enabling you to quickly start the ELT process rather than spending time setting up the environment. Free to use. Conclusion  There are a ton of small services that aren’t supported on traditional data pipeline platforms.

Azure 370

Azure Data Science Data Pipeline Data Engineering 370

phData

JUNE 14, 2023

In recent years, data engineering teams working with the Snowflake Data Cloud platform have embraced the continuous integration/continuous delivery (CI/CD) software development process to develop data products and manage ETL/ELT workloads more efficiently. What Are the Benefits of CI/CD Pipeline For Snowflake?

Data Pipeline 52

Data Pipeline Database SQL Data Engineering 52

phData

JULY 4, 2024

Because it runs Snowflake SQL from an easy-to-use, code-first GUI interface, it can take advantage of everything Snowflake offers, even if the feature is brand new. This blog will cover creating customized nodes in Coalesce, what new advanced features can already be used as nodes, and how to create them as part of your data pipeline.

SQL 52

SQL Data Pipeline Data Engineering Data Engineer 52

phData

JULY 12, 2023

Unlike traditional methods that rely on complex SQL queries for orchestration, Matillion Jobs provides a more streamlined approach. By converting SQL scripts into Matillion Jobs , users can take advantage of the platform’s advanced features for job orchestration, scheduling, and sharing. In our case, this table is “orders.”

SQL 52

SQL ETL Database Data Pipeline 52

phData

APRIL 21, 2023

Unlike traditional methods that rely on complex SQL queries for orchestration, Matillion Jobs provide a more streamlined approach. By converting SQL scripts into Matillion Jobs , users can take advantage of the platform’s advanced features for job orchestration, scheduling, and sharing. In our case, this table is “orders.”

SQL 52

SQL ETL Database Data Pipeline 52

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

SQL 52

SQL ML ML Python 52

phData

JULY 2, 2024

Snowpark, offered by the Snowflake AI Data Cloud , consists of libraries and runtimes that enable secure deployment and processing of non-SQL code, such as Python, Java, and Scala. Developers can seamlessly build data pipelines, ML models, and data applications with User-Defined Functions and Stored Procedures.

Python 52

Python SQL Data Pipeline ML 52

ODSC - Open Data Science

FEBRUARY 17, 2023

Cloud Computing, APIs, and Data Engineering NLP experts don’t go straight into conducting sentiment analysis on their personal laptops. Data Engineering Platforms Spark is still the leader for data pipelines but other platforms are gaining ground. Knowing some SQL is also essential.

Deep Learning 111

Deep Learning Deep Learning Data Science Natural Language Processing 111

phData

MARCH 8, 2023

It allows organizations to easily connect their disparate data sources without having to manage any infrastructure. Fivetran’s automated data movement platform simplifies the ETL (extract, transform, load) process by automating most of the time-consuming tasks of ETL that data engineers would typically do.

Data Warehouse 96

Data Warehouse Data Engineering Data Engineer Data Engineering 96

phData

AUGUST 2, 2024

Snowflake AI Data Cloud is one of the most powerful platforms, including storage services supporting complex data. Integrating Snowflake with dbt adds another layer of automation and control to the data pipeline. Snowflake stored procedures and dbt Hooks are essential to modern data engineering and analytics workflows.

Data Pipeline 52

Data Pipeline Python Database SQL 52

Data Science Dojo

JULY 3, 2024

Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python. Databases and SQL : Managing and querying relational databases using SQL, as well as working with NoSQL databases like MongoDB.

Data Science 195

Data Science Machine Learning Machine Learning Data Visualization 195

Applied Data Science

AUGUST 2, 2021

Automation Automating data pipelines and models ➡️ 6. Team Building the right data science team is complex. With a range of role types available, how do you find the perfect balance of Data Scientists , Data Engineers and Data Analysts to include in your team? Big Ideas What to look out for in 2022 1.

Data Science 130

Data Science Data Scientist Data Analyst Machine Learning 130

phData

NOVEMBER 2, 2023

Managing data pipelines efficiently is paramount for any organization. The Snowflake Data Cloud has introduced a groundbreaking feature that promises to simplify and supercharge this process: Snowflake Dynamic Tables. Flexibility: Dynamic tables allow batch and streaming pipelines to be specified in the same way.

Data Pipeline 52

Data Pipeline SQL Data Warehouse Data Engineering 52

phData

JULY 17, 2023

This is where Fivetran and the Modern Data Stack come in. Fivetran is a fully-automated, zero-maintenance data pipeline tool that automates the ETL process from data sources to your cloud warehouse. Centralize Many Different Data Sources Into a Single Cloud-Based Target (i.e. What is Fivetran?

Data Warehouse 52

Data Warehouse Data Pipeline Cloud Data ETL 52

ODSC - Open Data Science

FEBRUARY 2, 2023

Computer Science and Computer Engineering Similar to knowing statistics and math, a data scientist should know the fundamentals of computer science as well. While knowing Python, R, and SQL are expected, you’ll need to go beyond that. Big Data As datasets become larger and more complex, knowing how to work with them will be key.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

Pickl AI

JUNE 7, 2024

Integration: Airflow integrates seamlessly with other data engineering and Data Science tools like Apache Spark and Pandas. IBM Infosphere DataStage IBM Infosphere DataStage is an enterprise-level ETL tool that enables users to design, develop, and run data pipelines. How to drop a database in SQL server?

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

phData

OCTOBER 25, 2024

It’s common to have terabytes of data in most data warehouses, data quality monitoring is often challenging and cost-intensive due to dependencies on multiple tools and eventually ignored. This results in poor credibility and data consistency after some time, leading businesses to mistrust the data pipelines and processes.

Data Quality 52

Data Quality Data Pipeline Data Governance Database 52

phData

JULY 1, 2024

Below are several of the upcoming features announced during the Summit: Cortex Analyst : this upcoming feature, built with Meta’s Llama 3 and Mistral Large models, allows business users to chat with their data on Snowflake. Snowflake Copilot, soon-to-be GA, allows technical users to convert questions into SQL. schemas["my_schema"].tables.create(my_table)

Python 52

Python Database Data Pipeline SQL 52

phData

JUNE 26, 2024

Cleaning and preparing the data Raw data typically shouldn’t be used in machine learning models as it’ll throw off the prediction. Data engineers can prepare the data by removing duplicates, dealing with outliers, standardizing data types and precision between data sets, and joining data sets together.

Machine Learning 52

Machine Learning Machine Learning Data Engineering Data Engineer 52

Precisely

AUGUST 15, 2024

Key Players in AI Development Enterprises increasingly rely on AI to automate and enhance their data engineering workflows, making data more ready for building, training, and deploying AI applications. Data engineers focus on tasks like cleansing and managing data, ensuring its quality and readiness for AI applications.

AI 64

AI AI Data Quality Data Engineering 64

phData

MARCH 1, 2024

Sample CSV files (download files here ) Step 1: Load Sample CSV Files Into the Internal Stage Location Open the SQL worksheet and create a stage if it doesn’t exist. From the homepage: Data > Databases > Select your database/schema and select stages. Go back to the SQL worksheet and verify if the files exist.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Alation

APRIL 4, 2023

However, the race to the cloud has also created challenges for data users everywhere, including: Cloud migration is expensive, migrating sensitive data is risky, and navigating between on-prem sources is often confusing for users. This empowers users to judge data’s quality and fitness for purpose quickly.

DataOps 52

DataOps Data Engineering Data Engineer Data Engineering 52

phData

AUGUST 10, 2023

That said, dbt provides the ability to generate data vault models and also allows you to write your data transformations using SQL and code-reusable macros powered by Jinja2 to run your data pipelines in a clean and efficient way. The most important reason for using DBT in Data Vault 2.0

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Dataversity

SEPTEMBER 28, 2022

Data engineering is a fascinating and fulfilling career – you are at the helm of every business operation that requires data, and as long as users generate data, businesses will always need data engineers. The journey to becoming a successful data engineer […].

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 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. A DataFrame is like a query that must be evaluated to retrieve data. An action causes the DataFrame to be evaluated and sends the corresponding SQL statement to the server for execution.

Python 52

Python ML ML SQL 52

phData

SEPTEMBER 2, 2024

Best practices are a pivotal part of any software development, and data engineering is no exception. This ensures the data pipelines we create are robust, durable, and secure, providing the desired data to the organization effectively and consistently. Schedules cannot be edited or executed.

ETL 52

ETL Data Warehouse SQL Database 52

AWS Machine Learning Blog

OCTOBER 24, 2024

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

Data Warehouse 105

Data Warehouse Machine Learning Machine Learning Cloud Data 105