ODSC - Open Data Science

JANUARY 30, 2024

Data engineering is a hot topic in the AI industry right now. And as data’s complexity and volume grow, its importance across industries will only become more noticeable. But what exactly do data engineers do? So let’s do a quick overview of the job of data engineer, and maybe you might find a new interest.

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Data Engineering Data Engineering Data Engineering Data Engineer 52

phData

OCTOBER 25, 2024

“Quality over Quantity” is a phrase we hear regularly in life, but when it comes to the world of data, we often fail to adhere to this rule. Data Quality Monitoring implements quality checks in operational data processes to ensure that the data meets pre-defined standards and business rules.

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Mlearning.ai

MAY 16, 2023

Data engineering is a rapidly growing field that designs and develops systems that process and manage large amounts of data. There are various architectural design patterns in data engineering that are used to solve different data-related problems.

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Data Engineering Data Engineering 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. Read more to know.

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Data Engineering Data Engineering Data Engineering Data Engineer 52

IBM Journey to AI blog

OCTOBER 22, 2024

The ability to effectively deploy AI into production rests upon the strength of an organization’s data strategy because AI is only as strong as the data that underpins it. With that, a strategy that empowers less technical users and accelerates time to value for specialized data teams is critical.

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Alation

JANUARY 26, 2023

Governance can — and should — be the responsibility of every data user, though how that’s achieved will depend on the role within the organization. This article will focus on how data engineers can improve their approach to data governance. How can data engineers address these challenges directly?

Data Governance 52

Data Governance Data Engineering Data Engineering Data Engineering 52

AWS Machine Learning Blog

OCTOBER 9, 2024

The service, which was launched in March 2021, predates several popular AWS offerings that have anomaly detection, such as Amazon OpenSearch , Amazon CloudWatch , AWS Glue Data Quality , Amazon Redshift ML , and Amazon QuickSight. You can review the recommendations and augment rules from over 25 included data quality rules.

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Pickl AI

OCTOBER 17, 2024

Introduction ETL plays a crucial role in Data Management. This process enables organisations to gather data from various sources, transform it into a usable format, and load it into data warehouses or databases for analysis. Loading The transformed data is loaded into the target destination, such as a data warehouse.

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IBM Journey to AI blog

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. Ensuring data quality is made easier as a result.

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Data Quality Data Governance Data Wrangling Data Scientist 52

Alation

AUGUST 30, 2022

These range from data sources , including SaaS applications like Salesforce; ELT like Fivetran; cloud data warehouses like Snowflake; and data science and BI tools like Tableau. This expansive map of tools constitutes today’s modern data stack. In 2022.3, In 2022.3, But different users have different needs.

Data Governance 52

Data Governance Data Quality Tableau Data Analyst 52

Alation

AUGUST 11, 2022

The data warehouse and analytical data stores moved to the cloud and disaggregated into the data mesh. Today, the brightest minds in our industry are targeting the massive proliferation of data volumes and the accompanying but hard-to-find value locked within all that data. Architectures became fabrics.

Data Warehouse 52

Data Warehouse Data Engineering Data Engineering Data Engineering 52

Alation

OCTOBER 18, 2022

Data analysts and engineers use dbt to transform, test, and document data in the cloud data warehouse. Making this data visible in the data catalog will let data teams share their work, support re-use, and empower everyone to better understand and trust data.

Data Analyst 52

Data Analyst Data Engineering Data Engineering Data Engineering 52

phData

SEPTEMBER 19, 2023

A rigid data model such as Kimball or Data Vault would ruin this flexibility and essentially transform your data lake into a data warehouse. However, some flexible data modeling techniques can be used to allow for some organization while maintaining the ease of new data additions.

Data Lakes 52

Data Lakes Data Models Data Modeling Data Warehouse 52

Pickl AI

JUNE 7, 2024

Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Introduction In today’s business landscape, data integration is vital. Also Read: Top 10 Data Science tools for 2024. What is ETL?

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IBM Journey to AI blog

OCTOBER 2, 2024

These professionals encounter a range of issues when attempting to source the data they need, including: Data accessibility issues: The inability to locate and access specific data due to its location in siloed systems or the need for multiple permissions, resulting in bottlenecks and delays.

Business Intelligence 58

Business Intelligence Business Intelligence Power BI Data Quality 58

IBM Journey to AI blog

DECEMBER 7, 2023

Today, OLAP database systems have become comprehensive and integrated data analytics platforms, addressing the diverse needs of modern businesses. They are seamlessly integrated with cloud-based data warehouses, facilitating the collection, storage and analysis of data from various sources.

Data Preparation 72

Data Preparation Database Data Analysis Data Analysis 72

Alation

JULY 19, 2022

Modern data catalogs also facilitate data quality checks. Historically restricted to the purview of data engineers, data quality information is essential for all user groups to see. Data scientists often have different requirements for a data catalog than data analysts.

Data Scientist 59

Data Scientist Data Quality Data Science Data Analyst 59

Alation

MAY 16, 2023

Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible. Data Quality When using a data pipeline, data consistency, quality, and reliability are often greatly improved.

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Data Pipeline Data Governance Data Lakes Data Warehouse 69

Alation

SEPTEMBER 7, 2021

In the next section, let’s take a deeper look into how these key attributes help data scientists and analysts make faster, more informed decisions, while supporting stewards in their quest to scale governance policies on the Data Cloud easily. Find Trusted Data. Verifying quality is time consuming. Two problems arise.

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Data Governance Data Scientist Data Quality Data Profiling 59

The MLOps Blog

MAY 17, 2023

This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines. This ensures that the data which will be used for ML is accurate, reliable, and consistent.

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phData

MAY 14, 2024

They will focus on organizing data for quicker queries, optimizing virtual data warehouses, and refining query processes. The result is a data warehouse offering faster query responses, improved performance, and cost efficiency throughout your Snowflake account.

SQL 52

SQL Data Warehouse Data Engineer Data Engineering 52

phData

SEPTEMBER 20, 2023

A data mesh is a conceptual architectural approach for managing data in large organizations. Traditional data management approaches often involve centralizing data in a data warehouse or data lake, leading to challenges like data silos, data ownership issues, and data access and processing bottlenecks.

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Data Silos Database Data Quality Data Engineer 52

phData

JANUARY 24, 2024

Understanding Fivetran Fivetran is a user-friendly, code-free platform enabling customers to easily synchronize their data by automating extraction, transformation, and loading from many sources. Fivetran automates the time-consuming steps of the ELT process so your data engineers can focus on more impactful projects.

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Data Warehouse Data Pipeline Data Governance Cloud Data 52

IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

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phData

AUGUST 10, 2023

Data Vault - Data Lifecycle Architecturally, let’s understand the data lifecycle in the data vault into the following layers, which play a key role in choosing the right pattern and tools to implement. Data Acquisition: Extracting data from source systems and making it accessible.

SQL 52

SQL Data Observability Data Quality Data Pipeline 52

Alation

JANUARY 13, 2022

Data mesh forgoes technology edicts and instead argues for “decentralized data ownership” and the need to treat “data as a product”. Gartner on Data Fabric. Moreover, data catalogs play a central role in both data fabric and data mesh. Let’s turn our attention now to data mesh.

Data Lakes 59

Data Lakes Data Governance Data Quality Data Warehouse 59

Alation

MAY 16, 2023

Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible. Data Quality When using a data pipeline, data consistency, quality, and reliability are often greatly improved.

Data Pipeline 40

Data Pipeline Data Governance Data Lakes Data Warehouse 40

phData

OCTOBER 12, 2022

But maybe your business users want to be able to know if the data they’re consuming is fresh and up to their standards for data quality. dbt Cloud also gives your end users certainty that the data they’re using to make decisions is clean and current. Our team of data experts are happy to assist.

SQL 52

SQL Data Warehouse Data Visualization Cloud Data 52

phData

MAY 6, 2024

Data Quality Next, dive into the details of your data. Another benefit of deterministic matching is that the process to build these identities is relatively simple, and tools your teams might already use, like SQL and dbt , can efficiently manage this process within your cloud data warehouse.

Data Lakes 52

Data Lakes Data Warehouse Cloud Data SQL 52

phData

SEPTEMBER 27, 2024

Data Quality Management : Persistent staging provides a clear demarcation between raw and processed customer data. This makes it easier to implement and manage data quality processes, ensuring your marketing efforts are based on clean, reliable data. Of course, it’s not without its challenges.

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Data Models Data Modeling Apache Kafka Data Lakes 52

DagsHub

JANUARY 27, 2025

Text Data Labeling Techniques Text data labeling is a nuanced process, where success lies in finding the right balance between human expertise and automatic efficiency for each specific use case. Improve your data quality for better AI Easily curate and annotate your vision, audio, and document data with a single platform.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Supervised Learning 52

The MLOps Blog

MARCH 15, 2023

For small-scale/low-value deployments, there might not be many items to focus on, but as the scale and reach of deployment go up, data governance becomes crucial. This includes data quality, privacy, and compliance. Two Data Scientists: Responsible for setting up the ML models training and experimentation pipelines.

AWS 52

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phData

JULY 18, 2023

With the birth of cloud data warehouses, data applications, and generative AI , processing large volumes of data faster and cheaper is more approachable and desired than ever. First up, let’s dive into the foundation of every Modern Data Stack, a cloud-based data warehouse.

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Data Warehouse Analytics Analytics Cloud Data 52

AWS Machine Learning Blog

APRIL 3, 2025

When needed, the system can access an ODAP data warehouse to retrieve additional information. The company aims to integrate additional data sources, including other mission-critical systems, into ODAP. About the Authors Emrah Kaya is Data Engineering Manager at Omron Europe and Platform Lead for ODAP Project.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly. It promotes a disciplined approach to data modeling, making it easier to ensure data quality and consistency across the ML pipelines. Saurabh Gupta is a Principal Engineer at Zeta Global.

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IBM Journey to AI blog

JULY 27, 2023

Data quality strongly impacts the quality and usefulness of content produced by an AI model, underscoring the significance of addressing data challenges. It provides the combination of data lake flexibility and data warehouse performance to help to scale AI.

AI 69

AI AI Data Scientist Data Quality 69

phData

OCTOBER 17, 2023

In our previous blog , we discussed how Fivetran and dbt scale for any data volume and workload, both small and large. Now, you might be wondering what these tools can do for your data team and the efficiency of your organization as a whole. Can these tools help reduce the time our data engineers spend fixing things?

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ETL Data Pipeline Data Engineer Data Engineering 52