IBM Data Science in Practice

JANUARY 13, 2025

The blog post explains how the Internal Cloud Analytics team leveraged cloud resources like Code-Engine to improve, refine, and scale the data pipelines. Background One of the Analytics teams tasks is to load data from multiple sources and unify it into a data warehouse. Thus, it has only a minimal footprint.

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AWS Machine Learning Blog

DECEMBER 4, 2024

They have structured data such as sales transactions and revenue metrics stored in databases, alongside unstructured data such as customer reviews and marketing reports collected from various channels. Use Amazon Athena SQL queries to provide insights.

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AWS Machine Learning Blog

MARCH 14, 2024

Using structured data to answer questions requires a way to effectively extract data that’s relevant to a user’s query. We formulated a text-to-SQL approach where by a user’s natural language query is converted to a SQL statement using an LLM. The SQL is run by Amazon Athena to return the relevant data.

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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.

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Pickl AI

MARCH 19, 2025

Summary: Data engineering tools streamline data collection, storage, and processing. Tools like Python, SQL, Apache Spark, and Snowflake help engineers automate workflows and improve efficiency. Learning these tools is crucial for building scalable data pipelines.

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Smart Data Collective

SEPTEMBER 26, 2021

The raw data can be fed into a database or data warehouse. An analyst can examine the data using business intelligence tools to derive useful information. . To arrange your data and keep it raw, you need to: Make sure the data pipeline is simple so you can easily move data from point A to point B.

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phData

APRIL 28, 2025

Intuitive Workflow Design Workflows should be easy to follow and visually organized, much like clean, well-structured SQL or Python code. WHERE d.name = 'Sales'; Matillion is designed as a no/low-code ELT tool, so lets leave the SQL deep dive for another time and focus on making workflows as clean and intuitive as possible!

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phData

NOVEMBER 4, 2024

With all this packaged into a well-governed platform, Snowflake continues to set the standard for data warehousing and beyond. Snowflake supports data sharing and collaboration across organizations without the need for complex data pipelines.

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ODSC - Open Data Science

FEBRUARY 24, 2023

Great Expectations GitHub | Website Great Expectations (GX) helps data teams build a shared understanding of their data through quality testing, documentation, and profiling. With Great Expectations , data teams can express what they “expect” from their data using simple assertions.

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phData

AUGUST 8, 2024

Putting the T for Transformation in ELT (ETL) is essential to any data pipeline. After extracting and loading your data into the Snowflake AI Data Cloud , you may wonder how best to transform it. Luckily, Snowflake answers this question with many features designed to transform your data for all your analytic use cases.

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The MLOps Blog

JUNE 27, 2023

User support arrangements Consider the availability and quality of support from the provider or vendor, including documentation, tutorials, forums, customer service, etc. Kubeflow integrates with popular ML frameworks, supports versioning and collaboration, and simplifies the deployment and management of ML pipelines on Kubernetes clusters.

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phData

MARCH 8, 2023

By using Fivetran, businesses can reduce the time and resources required for data integration, enabling them to focus on extracting insights from the data rather than managing the ELT process. Building data pipelines manually is an expensive and time-consuming process. Why Use Fivetran?

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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

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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.

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Tableau

OCTOBER 8, 2021

Our continued investments in connectivity with Google technologies help ensure your data is secure, governed, and scalable. Tableau’s lightning-fast Google BigQuery connector allows customers to engineer optimized data pipelines with direct connections that power business-critical reporting. Direct connection to Google BigQuery.

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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.

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Pickl AI

APRIL 21, 2025

Semi-Structured Data: Data that has some organizational properties but doesn’t fit a rigid database structure (like emails, XML files, or JSON data used by websites). Unstructured Data: Data with no predefined format (like text documents, social media posts, images, audio files, videos).

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phData

DECEMBER 4, 2023

For greater detail, see the Snowflake documentation. Copy Into When loading data into Snowflake, the very first and most important rule to follow is: do not load data with SQL inserts! Loading small amounts of data is cumbersome and costly: Each insert is slow — and time is credits.

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phData

APRIL 29, 2024

SQL Server – The SQL Server connector, another widely-used database-type connector, provides similar functionality but is tailored for Microsoft’s SQL Server. The phData team achieved a major milestone by successfully setting up a secure end-to-end data pipeline for a substantial healthcare enterprise.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly.

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phData

JULY 1, 2024

Snowflake Copilot, soon-to-be GA, allows technical users to convert questions into SQL. Cortex Search : This feature provides a search solution that Snowflake fully manages from data ingestion, embedding, retrieval, reranking, and generation. At the same time, Cortex Analysts will be able to provide the answers to business questions.

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DagsHub

DECEMBER 11, 2023

It does not support the ‘dvc repro’ command to reproduce its data pipeline. DVC Released in 2017, Data Version Control ( DVC for short) is an open-source tool created by iterative. Dolt Created in 2019, Dolt is an open-source tool for managing SQL databases that uses version control similar to Git.

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phData

MARCH 20, 2024

dbt’s SQL-based approach democratizes data transformation. However, python and other programming languages edge out SQL with its metaprogramming capabilities. dbt’s Jinja integration bridges the gap between the expressiveness of Python and the familiarity of SQL. What is Jinja? Round 11.123 | round(1) 11.1

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Pickl AI

JUNE 7, 2024

Open-Source Community: Airflow benefits from an active open-source community and extensive documentation. IBM Infosphere DataStage IBM Infosphere DataStage is an enterprise-level ETL tool that enables users to design, develop, and run data pipelines. Scalability: Designed to handle large volumes of data efficiently.

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AWS Machine Learning Blog

SEPTEMBER 24, 2024

An optional CloudFormation stack to deploy a data pipeline to enable a conversation analytics dashboard. When you’re done, the top level of your S3 bucket should contain six folders, each containing a single Word or PDF document. You might want to peruse the sample documents you uploaded for some ideas about questions to ask.

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Pickl AI

OCTOBER 17, 2024

This section outlines key practices focused on automation, monitoring and optimisation, scalability, documentation, and governance. Automation Automation plays a pivotal role in streamlining ETL processes, reducing the need for manual intervention, and ensuring consistent data availability.

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Tableau

OCTOBER 8, 2021

Our continued investments in connectivity with Google technologies help ensure your data is secure, governed, and scalable. . Tableau’s lightning-fast Google BigQuery connector allows customers to engineer optimized data pipelines with direct connections that power business-critical reporting.

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Mlearning.ai

JUNE 16, 2023

Functional and non-functional requirements need to be documented clearly, which architecture design will be based on and support. A typical SDLC has following stages: Stage1: Planning and requirement analysis, defining Requirements Gather requirement from end customer. Then software development phases are planned to deliver the software.

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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. In contrast, such traditional query languages struggle to interpret unstructured data. Storage Tools To work with unstructured data, you need to store it.

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phData

MAY 25, 2023

The June 2021 release of Power BI Desktop introduced Custom SQL queries to Snowflake in DirectQuery mode. In 2021, Microsoft enabled Custom SQL queries to be run to Snowflake in DirectQuery mode further enhancing the connection capabilities between the platforms.

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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.

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phData

OCTOBER 17, 2024

dbt offers a SQL-first transformation workflow that lets teams build data transformation pipelines while following software engineering best practices like CI/CD, modularity, and documentation. Aside from migrations, Data Source is also great for data quality checks and can generate data pipelines.

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Heartbeat

JANUARY 5, 2024

Introduction to LangChain for Including AI from Large Language Models (LLMs) Inside Data Applications and Data Pipelines This article will provide an overview of LangChain, the problems it addresses, its use cases, and some of its limitations. Memory : Storing and retrieving data while conversing.

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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.

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phData

OCTOBER 20, 2023

Some of the databases supported by Fivetran are: Snowflake Data Cloud (BETA) MySQL PostgreSQL SAP ERP SQL Server Oracle In this blog, we will review how to pull Data from on-premise Systems using Fivetran to a specific target or destination. You can find more information about them in their official documentation.

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Mlearning.ai

JULY 10, 2023

There are other options you can place, and as usual, I suggest you to reference the official documentation to learn more. In case of complex data pipelines, a combination of Materialized Views, Stored Procedures, and Scheduled Queries could be a better choice than to solely rely on Scheduled Queries by itself.

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Snorkel AI

JANUARY 24, 2023

Data can then be labeled programmatically using a data-centric AI workflow in Snorkel Flow to quickly generate high-quality training sets over complex, highly variable data. Snorkel Flow includes templates to classify and extract information from native PDFs, richly formatted documents, HTML data, conversational text, and more.

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