Smart Data Collective

OCTOBER 17, 2022

It may conflict with your data governance policy (more on that below), but it may be valuable in establishing a broader view of the data and directing you toward better data sets for your main models. Data pipeline maintenance. It adds a layer of bureaucracy to data engineering that you may like to avoid.

Big Data 117

Big Data Big Data Data Engineering Data Engineering 117

PyImageSearch

MARCH 20, 2023

Jump Right To The Downloads Section Training and Making Predictions with Siamese Networks and Triplet Loss In the second part of this series, we developed the modules required to build the data pipeline for our face recognition application. Figure 1: Overview of our Face Recognition Pipeline (source: image by the author).

Deep Learning 102

Deep Learning Deep Learning Data Pipeline Python 102

Flipboard

MARCH 6, 2023

In the previous tutorial of this series, we built the dataset and data pipeline for our Siamese Network based Face Recognition application. Specifically, we looked at an overview of triplet loss and discussed what kind of data samples are required to train our model with the triplet loss.

Deep Learning 144

Deep Learning Deep Learning Data Pipeline Computer Science 144

Flipboard

AUGUST 17, 2023

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development. With this Spark connector, you can easily ingest data to the feature group’s online and offline store from a Spark DataFrame.

ML 123

ML ML AWS Data Warehouse 123

Tableau

OCTOBER 8, 2021

With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics. Connecting directly to this semantic layer will help give customers access to critical business data in a safe, governed manner. Direct connection to Google BigQuery.

Tableau 137

Tableau Analytics Analytics Machine Learning 137

ODSC - Open Data Science

FEBRUARY 2, 2023

To get a better grip on those changes we reviewed over 25,000 data scientist job descriptions from that past year to find out what employers are looking for in 2023. Much of what we found was to be expected, though there were definitely a few surprises. You’ll see specific tools in the next section.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

Iguazio

JANUARY 22, 2024

Definitions: Foundation Models, Gen AI, and LLMs Before diving into the practice of productizing LLMs, let’s review the basic definitions of GenAI elements: Foundation Models (FMs) - Large deep learning models that are pre-trained with attention mechanisms on massive datasets. This helps cleanse the data.

Data Pipeline 52

Data Pipeline ML ML Data Warehouse 52

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

phData

JANUARY 19, 2024

Matillion’s Data Productivity Cloud is a versatile platform designed to increase the productivity of data teams. It provides a unified platform for creating and managing data pipelines that are effective for both coders and non-coders.

Data Pipeline 52

Data Pipeline Data Warehouse ETL Azure 52

DrivenData Labs

MAY 21, 2024

Hello from our new, friendly, welcoming, definitely not an AI overlord cookie logo! The second is to provide a directed acyclic graph (DAG) for data pipelining and model building. The vision for V2 is to give CCDS users more flexibility while still making a great template if you just put your hand over your eyes and hit enter.

Data Science 233

Data Science Python Data Scientist Data Warehouse 233

Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

phData

SEPTEMBER 13, 2023

Third-Party Tools Third-party tools like Matillion or Fivetran can help streamline the process of ingesting Salesforce data into Snowflake. With these tools, businesses can quickly set up data pipelines that automatically extract data from Salesforce and load it into Snowflake.

Tableau 52

Tableau Data Pipeline Data Silos Analytics 52

Tableau

OCTOBER 8, 2021

With its LookML modeling language, Looker provides a unique, modern approach to define governed and reusable data models to build a trusted foundation for analytics. Connecting directly to this semantic layer will help give customers access to critical business data in a safe, governed manner. Direct connection to Google BigQuery.

Tableau 98

Tableau Analytics Analytics Machine Learning 98

Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

Data Profiling 52

Data Profiling Data Quality Data Governance Data Pipeline 52

AWS Machine Learning Blog

SEPTEMBER 18, 2023

It integrates smoothly with other data processing libraries like Spark, Pandas, NumPy, and more, as well as ML frameworks like TensorFlow and PyTorch. This allows building end-to-end data pipelines and ML workflows on top of Ray. The goal is to make distributed data processing and ML easier for practitioners and researchers.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

AWS Machine Learning Blog

AUGUST 22, 2024

This makes managing and deploying these updates across a large-scale deployment pipeline while providing consistency and minimizing downtime a significant undertaking. Generative AI applications require continuous ingestion, preprocessing, and formatting of vast amounts of data from various sources.

ML 130

ML ML AI AI 130

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

AWS Machine Learning Blog

OCTOBER 24, 2024

The agent can generate SQL queries using natural language questions using a database schema DDL (data definition language for SQL) and execute them against a database instance for the database tier. We use Amazon Bedrock Agents with two knowledge bases for this assistant.

AWS 101

AWS SQL Database AI 101

IBM Journey to AI blog

JULY 1, 2024

Whenever anyone talks about data lineage and how to achieve it, the spotlight tends to shine on automation. This is expected, as automating the process of calculating and establishing lineage is crucial to understanding and maintaining a trustworthy system of data pipelines.

ETL 40

ETL Data Lakes Database Data Pipeline 40

phData

SEPTEMBER 15, 2023

Conclusion Integrating Salesforce data with Snowflake’s Data Cloud using Tableau CRM Sync Out can benefit organizations by consolidating internal and third-party data on a single platform, making it easier to find valuable insights while removing the challenges of data silos and movement.

Data Warehouse 52

Data Warehouse Tableau Data Silos Analytics 52

phData

JANUARY 10, 2024

The data source tool can also directly generate the Data Definition Language (DDL) for these tables as well if you decide not to use dbt! This allows you to better understand the existing structures that are in place and more accurately perform your migration (or generate documentation, everybody’s favorite!).

Data Warehouse 52

Data Warehouse Data Profiling Data Pipeline Database 52

Pickl AI

JUNE 7, 2024

It is a process for moving and managing data from various sources to a central data warehouse. This process ensures that data is accurate, consistent, and usable for analysis and reporting. Definition and Explanation of the ETL Process ETL is a data integration method that combines data from multiple sources.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

PyImageSearch

FEBRUARY 5, 2024

lossTracker=keras.metrics.Mean(name="loss"), ) Loading the Trained Siamese Model for Evaluation Now that we have completed the definition of our metrics function, let us load our trained Siamese model and evaluate its performance. On Line 29 , we get our modelPath , where we save our trained Siamese model after training.

Database 60

Database Data Pipeline Deep Learning Deep Learning 60

phData

MARCH 26, 2024

Explore phData's Snowflake Services Closing Snowflake’s Hybrid tables are a powerful new feature that can help organizations break down data silos and bring transactional and analytical data together in one platform. Hybrid tables can streamline data pipelines, reduce costs, and unlock deeper insights from data.

Clustering 52

Clustering Internet of Things Analytics Data Silos 52

The MLOps Blog

MARCH 28, 2024

We’ll explore how factors like batch size, framework selection, and the design of your data pipeline can profoundly impact the efficient utilization of GPUs. We need a well-optimized data pipeline to achieve this goal. The pipeline involves several steps. What should be the GPU usage?

Deep Learning 52

Deep Learning Deep Learning Data Pipeline Machine Learning 52

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. Data scientists and machine learning engineers need to collaborate to make sure that together with the model, they develop robust data pipelines.

Machine Learning 59

Machine Learning Machine Learning ML ML 59

phData

SEPTEMBER 5, 2023

Let’s briefly look at the key components and their roles in this process: Azure Data Factory (ADF) : ADF will serve as our data orchestration and integration platform. It enables us to create, schedule, and monitor the data pipeline, ensuring seamless movement of data between the various sources and destinations.

Azure 52

Azure Analytics Analytics Data Pipeline 52

phData

MARCH 1, 2023

When customers are looking to perform a migration, one of the first things that needs to occur is an assessment of the level of effort to migrate existing data definition language (DDL) and data markup language (DML). The first one we want to talk about is the Toolkit SQL analyze command.

SQL 52

SQL Data Pipeline Data Quality Database 52

Snorkel AI

JULY 3, 2023

As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs. Because of this, I’m always looking for ways to automate and improve our data pipelines. So why should we use data pipelines?

Exploratory Data Analysis 52

Exploratory Data Analysis Data Pipeline Machine Learning Machine Learning 52

Alation

SEPTEMBER 21, 2021

DataOps is about the continuous development, testing and deployment of data products, and the data itself. Intelligence about where data is, what it looks like, what it means and how it is flowing to combat the 3 D’s and help Gen-D visualize data pipelines from producer to consumer.

DataOps 52

DataOps Data Governance Data Pipeline 52

Snorkel AI

JULY 3, 2023

As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs. Because of this, I’m always looking for ways to automate and improve our data pipelines. So why should we use data pipelines?

Exploratory Data Analysis 52

Exploratory Data Analysis Data Pipeline Data Scientist Machine Learning 52

Snorkel AI

JULY 3, 2023

As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs. Because of this, I’m always looking for ways to automate and improve our data pipelines. So why should we use data pipelines?

Exploratory Data Analysis 52

Exploratory Data Analysis Data Pipeline Data Scientist Machine Learning 52

phData

MARCH 1, 2024

CREATE TABLE CUSTOMER_TABLE USING TEMPLATE ( SELECT ARRAY_AGG(OBJECT_CONSTRUCT(*)) WITHIN GROUP (ORDER BY order_id) FROM TABLE( INFER_SCHEMA( LOCATION=>'@csv_stage/customer-initial-data.csv', FILE_FORMAT=>'csv_format' ) )); This creates CUSTOMER_TABLE with the definition generated by INFER_SCHEMA.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

phData

SEPTEMBER 7, 2023

We’ve had many customers performing migrations between these platforms, and as a result, they have a lot of Data Definition Language (DDL) and Data Markup Language (DML) that needs to be translated between SQL dialects. Let’s take a look at some of the more interesting translations.

SQL 52

SQL Data Profiling Data Pipeline Database 52

Alation

MAY 31, 2023

American Family Insurance: Governance by Design – Not as an Afterthought Who: Anil Kumar Kunden , Information Standards, Governance and Quality Specialist at AmFam Group When: Wednesday, June 7, at 2:45 PM Why attend: Learn how to automate and accelerate data pipeline creation and maintenance with data governance, AKA metadata normalization.

Data Governance 52

Data Governance DataOps Data Pipeline Business Intelligence 52

PyImageSearch

JUNE 5, 2023

Specifically, we will develop our data pipeline, implement the loss functions discussed in Part 1 and write our own code to train the CycleGAN model end-to-end using Keras and TensorFlow. With this, we finish the definition of our TrainMonitor class. Finally, we combine and consolidate our entire training data (i.e.,

Deep Learning 56

Deep Learning Deep Learning Data Pipeline Python 56

DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

Machine Learning 52

Machine Learning Machine Learning AI AI 52