Tableau

JUNE 8, 2021

In many of the conversations we have with IT and business leaders, there is a sense of frustration about the speed of time-to-value for big data and data science projects. We often hear that organizations have invested in data science capabilities but are struggling to operationalize their machine learning models.

Tableau 102

Tableau Data Lakes Data Warehouse SQL 102

ODSC - Open Data Science

SEPTEMBER 29, 2023

Data management problems can also lead to data silos; disparate collections of databases that don’t communicate with each other, leading to flawed analysis based on incomplete or incorrect datasets. One way to address this is to implement a data lake: a large and complex database of diverse datasets all stored in their original format.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

Pickl AI

JULY 8, 2024

Summary: This blog explains how to build efficient data pipelines, detailing each step from data collection to final delivery. Introduction Data pipelines play a pivotal role in modern data architecture by seamlessly transporting and transforming raw data into valuable insights.

Data Pipeline 52

Data Pipeline Data Quality Database Apache Kafka 52

ODSC - Open Data Science

OCTOBER 13, 2024

Be sure to check out her talk, “ Don’t Go Over the Deep End: Building an Effective OSS Management Layer for Your Data Lake ,” there! Managing a data lake can often feel like being lost at sea — especially when dealing with both structured and unstructured data.

Data Lakes 40

Data Lakes Database Data Pipeline SQL 40

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 post highlights how Twilio enabled natural language-driven data exploration of business intelligence (BI) data with RAG and Amazon Bedrock.

SQL 124

SQL Data Lakes Data Analyst AWS 124

AWS Machine Learning Blog

APRIL 3, 2025

Amazon AppFlow was used to facilitate the smooth and secure transfer of data from various sources into ODAP. Additionally, Amazon Simple Storage Service (Amazon S3) served as the central data lake, providing a scalable and cost-effective storage solution for the diverse data types collected from different systems.

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AWS Data Governance Data Silos SQL 76

DagsHub

DECEMBER 11, 2023

The following points illustrates some of the main reasons why data versioning is crucial to the success of any data science and machine learning project: Storage space One of the reasons of versioning data is to be able to keep track of multiple versions of the same data which obviously need to be stored as well.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Database 98

ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

ODSC - Open Data Science

DECEMBER 19, 2023

Data Engineer Data engineers are responsible for the end-to-end process of collecting, storing, and processing data. They use their knowledge of data warehousing, data lakes, and big data technologies to build and maintain data pipelines. Get your pass today!

Data Scientist 96

Data Scientist Machine Learning Machine Learning AI 96

ODSC - Open Data Science

FEBRUARY 24, 2023

Great Expectations provides support for different data backends such as flat file formats, SQL databases, Pandas dataframes and Sparks, and comes with built-in notification and data documentation functionality. At ODSC East 2023, we have a number of sessions related to data visualization and data exploration tools.

Exploratory Data Analysis 126

Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

phData

NOVEMBER 9, 2023

A data warehouse is a centralized and structured storage system that enables organizations to efficiently store, manage, and analyze large volumes of data for business intelligence and reporting purposes. What is a Data Lake? What is the Difference Between a Data Lake and a Data Warehouse?

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Data Warehouse Data Lakes Clustering Cloud Data 98

The MLOps Blog

JUNE 27, 2023

With built-in components and integration with Google Cloud services, Vertex AI simplifies the end-to-end machine learning process, making it easier for data science teams to build and deploy models at scale. Metaflow Metaflow helps data scientists and machine learning engineers build, manage, and deploy data science projects.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Tableau

JUNE 8, 2021

In many of the conversations we have with IT and business leaders, there is a sense of frustration about the speed of time-to-value for big data and data science projects. We often hear that organizations have invested in data science capabilities but are struggling to operationalize their machine learning models.

Tableau 52

Tableau Data Lakes Data Warehouse SQL 52

ODSC - Open Data Science

JANUARY 30, 2024

Not only does it involve the process of collecting, storing, and processing data so that it can be used for analysis and decision-making, but these professionals are responsible for building and maintaining the infrastructure that makes this possible; and so much more. Think of data engineers as the architects of the data ecosystem.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

ODSC - Open Data Science

FEBRUARY 14, 2023

It also addresses the strategies and best practices for implementing a data mesh. Applying Engineering Best Practices in Data Lakes Architectures Einat Orr | Ceo and Co-Founder | Treeverse This talk examines why agile methodology, continuous integration, and continuous deployment and production monitoring are essential for data lakes.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

ODSC - Open Data Science

OCTOBER 15, 2023

Institute of Analytics The Institute of Analytics is a non-profit organization that provides data science and analytics courses, workshops, certifications, research, and development. The courses and workshops cover a wide range of topics, from basic data science concepts to advanced machine learning techniques.

Machine Learning 52

Machine Learning Machine Learning Data Pipeline AI 52

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.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

DataRobot Blog

OCTOBER 3, 2017

This highlights the two companies’ shared vision on self-service data discovery with an emphasis on collaboration and data governance. 2) When data becomes information, many (incremental) use cases surface. We look at data as an asset, regardless of whether the use case is AML/fraud or new revenue.

Data Lakes 52

Data Lakes Azure Data Pipeline Hadoop 52

ODSC - Open Data Science

FEBRUARY 15, 2024

These systems represent data as knowledge graphs and implement graph traversal algorithms to help find content in massive datasets. These systems are not only useful for a wide range of industries, they are fun for data engineers to work on. So get your pass today, and keep yourself ahead of the curve.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Flipboard

NOVEMBER 24, 2023

The service streamlines ML development and production workflows (MLOps) across BMW by providing a cost-efficient and scalable development environment that facilitates seamless collaboration between data science and engineering teams worldwide. This results in faster experimentation and shorter idea validation cycles.

ML 149

ML ML AWS AI 149

Pickl AI

AUGUST 1, 2023

Data Engineering plays a critical role in enabling organizations to efficiently collect, store, process, and analyze large volumes of data. It is a field of expertise within the broader domain of data management and Data Science. Future of Data Engineering The Data Engineering market will expand from $18.2

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. If you want to do the process in a low-code/no-code way, you can follow option C.

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ML ML AWS Data Warehouse 123

Pickl AI

SEPTEMBER 5, 2024

Summary: This blog provides a comprehensive roadmap for aspiring Azure Data Scientists, outlining the essential skills, certifications, and steps to build a successful career in Data Science using Microsoft Azure. Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage.

Azure 52

Azure Data Scientist Data Science Machine Learning 52

The MLOps Blog

JANUARY 23, 2023

These tools may have their own versioning system, which can be difficult to integrate with a broader data version control system. For instance, our data lake could contain a variety of relational and non-relational databases, files in different formats, and data stored using different cloud providers. DVC Git LFS neptune.ai

ML 52

ML ML Data Lakes Machine Learning 52

IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. What does a modern data architecture do for your business?

Data Quality 52

Data Quality Data Lakes Data Warehouse Big Data 52

IBM Journey to AI blog

OCTOBER 16, 2023

How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools. A data store lets a business connect existing data with new data and discover new insights with real-time analytics and business intelligence.

AI 88

AI AI Data Warehouse ML 88

Iguazio

JANUARY 16, 2024

Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization, including a large number of data and data science professionals. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team.

ML 52

ML ML DataOps Data Scientist 52

IBM Journey to AI blog

JULY 17, 2023

By supporting open-source frameworks and tools for code-based, automated and visual data science capabilities — all in a secure, trusted studio environment — we’re already seeing excitement from companies ready to use both foundation models and machine learning to accomplish key tasks.

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AI AI Machine Learning Machine Learning 74

Dataconomy

FEBRUARY 23, 2023

This involves creating data validation rules, monitoring data quality, and implementing processes to correct any errors that are identified. Creating data pipelines and workflows Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently.

Big Data 91

Big Data Big Data Data Engineering Data Engineer 91

AWS Machine Learning Blog

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

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AWS ML ML Machine Learning 130

Iguazio

JANUARY 16, 2024

Since AI is a central pillar of their value offering, Sense has invested heavily in a robust engineering organization including a large number of data and AI professionals. This includes a data team, an analytics team, DevOps, AI/ML, and a data science team. First, the data lake is fed from a number of data sources.

ML 52

ML ML DataOps Data Scientist 52

phData

FEBRUARY 24, 2023

Alteryx provides organizations with an opportunity to automate access to data, analytics , data science, and process automation all in one, end-to-end platform. Its capabilities can be split into the following topics: automating inputs & outputs, data preparation, data enrichment, and data science.

Analytics 52

Analytics Analytics Database Python 52

phData

DECEMBER 4, 2023

If you answer “yes” to any of these questions, you will need cloud storage, such as Amazon AWS’s S3, Azure Data Lake Storage or GCP’s Google Storage. Data Pipelines “Data pipeline” means moving data in a consistent, secure, and reliable way at some frequency that meets your requirements.

Clustering 98

Clustering Database SQL Data Pipeline 98

AWS Machine Learning Blog

DECEMBER 20, 2023

A novel approach to solve this complex security analytics scenario combines the ingestion and storage of security data using Amazon Security Lake and analyzing the security data with machine learning (ML) using Amazon SageMaker. His core area of expertise include Technology Strategy, Data Analytics, and Data Science.

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AWS ML ML Algorithm 133

phData

AUGUST 20, 2024

This helps manage data drift and maintain the integrity of training and test sets. Data Lineage: Keeping a record of data transformations and preprocessing steps to ensure the data pipeline is reproducible and auditable. For more details, see the DVC Data Pipelines documentation. Contact phData Today!

Machine Learning 52

Machine Learning Machine Learning ML ML 52

AWS Machine Learning Blog

APRIL 12, 2023

With this service, industrial sensors, smart meters, and OPC UA servers can be connected to an AWS data lake with just a few clicks. It’s an easy way to run analytics on IoT data to gain accurate insights. After the model was trained, we deployed the model and started sending live data to Lookout for Equipment.

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AWS ML ML Machine Learning 107

Alation

JULY 7, 2022

To answer these questions we need to look at how data roles within the job market have evolved, and how academic programs have changed to meet new workforce demands. In the 2010s, the growing scope of the data landscape gave rise to a new profession: the data scientist. A lack of data literacy slows down the process.

Data Scientist 52

Data Scientist Data Analyst Analytics Analytics 52