IBM Journey to AI blog

NOVEMBER 3, 2023

Apache Kafka and Apache Flink working together Anyone who is familiar with the stream processing ecosystem is familiar with Apache Kafka: the de-facto enterprise standard for open-source event streaming. Apache Kafka streams get data to where it needs to go, but these capabilities are not maximized when Apache Kafka is deployed in isolation.

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The MLOps Blog

MARCH 15, 2023

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process.

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phData

APRIL 29, 2024

Recognizing these specific needs, Fivetran has developed a range of connectors, including dedicated applications, databases, files, and events, which can accommodate the diverse formats used by healthcare systems. Addressing these needs may pose challenges that lead to the implementation of custom solutions rather than a uniform approach.

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IBM Journey to AI blog

AUGUST 29, 2024

Event-driven businesses across all industries thrive on real-time data, enabling companies to act on events as they happen rather than after the fact. Flink jobs, designed to process continuous data streams, are key to making this possible. They are able to adapt to changing demands quickly to seize new opportunities.

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phData

SEPTEMBER 7, 2023

Over the last month, we’ve been heavily focused on adding additional support for SQL translations to our SQL Translations tool. Specifically, we’ve been introducing fixes and features for our Microsoft SQL Server to Snowflake translation. This is where the SQL Translation tool can be a massive accelerator for your migration.

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IBM Journey to AI blog

SEPTEMBER 4, 2024

Apache Kafka is an open-source , distributed streaming platform that allows developers to build real-time, event-driven applications. With Apache Kafka, developers can build applications that continuously use streaming data records and deliver real-time experiences to users.

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Pickl AI

NOVEMBER 4, 2024

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. They are crucial in ensuring data is readily available for analysis and reporting.

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phData

JULY 29, 2023

We’ve been focusing on two key areas: Microsoft SQL Server to Snowflake Data Cloud SQL translations and our new Advisor tool within the phData Toolkit. Operational Risks identify operational risks such as data loss or failures in the event of an unforeseen outage or disaster. Let’s dive in.

SQL 52

SQL Database Data Pipeline 52

phData

SEPTEMBER 5, 2023

Google Analytics 4 (GA4) is a powerful tool for collecting and analyzing website and app data that many businesses rely heavily on to make informed business decisions. However, there might be instances where you need to migrate the raw event data from GA4 to Snowflake for more in-depth analysis and business intelligence purposes.

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The MLOps Blog

JUNE 27, 2023

It could help you detect and prevent data pipeline failures, data drift, and anomalies. Montecarlo offers data quality checks, profiling, and monitoring capabilities to ensure high-quality and reliable data for machine learning and analytics. Flyte Flyte is a platform for orchestrating ML pipelines at scale.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.

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phData

JULY 1, 2024

In this blog, we will highlight some of the most important upcoming features and updates for those who could not attend the events, specifically around AI and developer tools. Snowflake Copilot, soon-to-be GA, allows technical users to convert questions into SQL. Furthermore, Snowflake Notebooks can also be run on a schedule.

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ODSC - Open Data Science

JANUARY 18, 2024

Data engineers will also work with data scientists to design and implement data pipelines; ensuring steady flows and minimal issues for data teams. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable. Interested in attending an ODSC event?

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phData

FEBRUARY 5, 2024

The tool converts the templated configuration into a set of SQL commands that are executed against the target Snowflake environment. Migrating Your Pipelines and Code It’s more than likely that your business has years of code being used in its data pipelines. It is also a helpful tool for learning a new SQL dialect.

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ODSC - Open Data Science

SEPTEMBER 29, 2023

Furthermore, we’ve developed data encryption and governance solutions for HPCC Systems to help secure data, ensure it is only accessed by appropriate personnel, and to create audit trails to ensure data security SLAs and regulations are met. It truly is an all-in-one data lake solution. Tell me more about ECL.

Data Lakes 52

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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.

Data Quality 52

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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. The most common example of such databases is where events are tracked.

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phData

OCTOBER 17, 2024

The DAGs can then be scheduled to run at specific intervals or triggered when an event occurs. It even offers a user-friendly interface to visualize the pipelines and monitor progress. The Data Source Tool can automate scanning DDL and profiling tables between source and target, comparing them, and then reporting findings.

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phData

SEPTEMBER 27, 2024

Methods that allow our customer data models to be as dynamic and flexible as the customers they represent. In this guide, we will explore concepts like transitional modeling for customer profiles, the power of event logs for customer behavior, persistent staging for raw customer data, real-time customer data capture, and much more.

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ODSC - Open Data Science

APRIL 26, 2023

Though scripted languages such as R and Python are at the top of the list of required skills for a data analyst, Excel is still one of the most important tools to be used. Because they are the most likely to communicate data insights, they’ll also need to know SQL, and visualization tools such as Power BI and Tableau as well.

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phData

MARCH 29, 2024

This evolved into the phData Toolkit , a collection of high-quality data applications to help you migrate, validate, optimize, and secure your data. Operational Risks: Uncover operational risks such as data loss or failures in the event of an unforeseen outage or disaster.

Data Engineering 52

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Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Data warehousing is a vital constituent of any business intelligence operation. Simplify and Win Experienced data engineers value simplicity.

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Data Warehouse Business Intelligence Business Intelligence Database 52

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. Python : Great for including AI in Python-based software or data pipelines.

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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.

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phData

AUGUST 5, 2024

For instance, differential privacy adds noise to query results as a means of preventing access to Personally Identifiable Information (PII) and running multi-party computations directly on encrypted data. Object Tagging Tags are schema-level objects that allow data stewards to monitor sensitive data for compliance, protection, or discovery.

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Pickl AI

SEPTEMBER 5, 2024

However, a master’s degree or specialised Data Science or Machine Learning courses can give you a competitive edge, offering advanced knowledge and practical experience. Essential Technical Skills Technical proficiency is at the heart of an Azure Data Scientist’s role.

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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. This text has a lot of information, but it is not structured.

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phData

FEBRUARY 14, 2023

What Are the Best Third-Party Data Ingestion Tools for Snowflake? Fivetran Fivetran is a tool dedicated to replicating applications, databases, events, and files into a high-performance data warehouse, such as Snowflake. Source data formats can only be Parquer, JSON, or Delimited Text (CSV, TSV, etc.).

Data Warehouse 59

Data Warehouse Azure AWS Database 59

ODSC - Open Data Science

JANUARY 7, 2025

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 is expected, youll need to go beyond that. Employers arent just looking for people who can program.

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Alation

APRIL 14, 2022

Data pipeline orchestration. Support for languages and SQL. Moving/integrating data in the cloud/data exploration and quality assessment. Supports the ability to interact with the actual data and perform analysis on it. Collaboration and governance. Low-code, no-code operation. Scheduling. Target Matching.

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DagsHub

AUGUST 23, 2024

Data Quality Dimensions Data quality dimensions are the criteria that are used to evaluate and measure the quality of data. These include the following: Accuracy indicates how correctly data reflects the real-world entities or events it represents. It is SQL-based and integrates well with modern data warehouses.

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phData

OCTOBER 28, 2024

However, if the tool supposes an option where we can write our custom programming code to implement features that cannot be achieved using the drag-and-drop components, it broadens the horizon of what we can do with our data pipelines. JV_STAGING_TBL} Here is what the outline of the pipeline looks like. 30 minutes).

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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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ODSC - Open Data Science

SEPTEMBER 20, 2023

Monday’s sessions will cover a wide range of topics, from Generative AI and LLMs to MLOps and Data Visualization. Finally, get ready for some All Hallows Eve fun with Halloween Data After Dark , featuring a costume contest, candy, and more. There will also be an in-person career expo where you can find your next job in data science!

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The MLOps Blog

MARCH 21, 2023

The service will consume the features in real time, generate predictions in near real-time , such as in an event processing pipeline, and write the outputs to a prediction queue. I have worked with customers where R and SQL were the first-class languages of their data science community.

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ODSC - Open Data Science

DECEMBER 9, 2024

Yet despite these rich capabilities, challenges stillarise The Fragmentation Challenge With so many modular open-source libraries and frameworks now available, effectively stitching together coherent data science application workflows poses a frequent headache for practitioners. So whats needed to smooth the path forward?

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