Data Engineering Weekly

MARCH 3, 2024

This approach enables deeper insights into complex datasets that LLMs have not been trained on, demonstrating substantial improvements in data understanding and thematic discovery. link] Nvidia: What Is Sovereign AI? This approach helps maintain accuracy, relevance, and compliance in generative AI applications.

Data Engineering 67

Data Engineering Data Engineer Pipeline-centric Engineering 67

Towards Data Science

JUNE 8, 2023

This post will focus on the most common team ownership models including: data engineering, data reliability engineering, analytics engineering, data quality analysts, and data governance teams. Why is data quality ownership important? The governance team treats every team output as a data product.

Data Governance 76

Data Governance Government Generalist Data Engineering 76

Monte Carlo

MARCH 13, 2024

Let’s dig in and explore the landscape of the top so-called “data quality tools” — what they are, what they’re not, and whether they’re the right first step towards more reliable data. Governance helps companies set important standards and achieve higher levels of data security, data accessibility, and data quality.

Data Governance 52

Data Governance Government Metadata Data 52

Monte Carlo

JANUARY 3, 2024

This is a widely shared sentiment across many data leaders I speak to. If the data team has suddenly surfaced customer-facing, secure data, then they’re on the hook. Data governance is a massive consideration and it’s a high bar to clear. away from your data infrastructure being GenAI ready. It’s 2024 now.

Technology 113

Technology Database Data Governance Data Engineering 113

Precisely

MAY 13, 2024

It synthesizes all the metadata around your organization’s data assets and arranges the information into a simple, easy-to-understand format. Questions to ask each vendor: Does your data integration solution provide access to the metadata? What data governance controls do your solutions have in place?

Data Integration 52

Data Integration Metadata Amazon Web Services Data Governance 52

Monte Carlo

MAY 14, 2024

Iceberg provides advanced features such as schema evolution, which allows modifications to a table’s schema without downtime, and snapshot isolation, which ensures data consistency. And that matters — because these new table formats are also introducing complexity in other ways.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

Databand.ai

AUGUST 30, 2023

This includes defining roles and responsibilities related to managing datasets and setting guidelines for metadata management. Data profiling: Regularly analyze dataset content to identify inconsistencies or errors. Automated profiling tools can quickly detect anomalies or patterns indicating potential dataset integrity issues.

Building 52

Building Data Cleanse Data Governance Datasets 52

Monte Carlo

JANUARY 10, 2024

Data Cleansing 3. Data Validation 4. Data Auditing 5. Data Governance 6. Use of Data Quality Tools Refresh your intrinsic data quality with data observability 1. Data Profiling Data profiling is getting to know your data, warts and quirks and secrets and all.

Data Cleanse 52

Data Cleanse Data Engineering Data Engineer Engineering 52

Databand.ai

AUGUST 30, 2023

Validity: Adherence to predefined formats, rules, or standards for each attribute within a dataset. Uniqueness: Ensuring that no duplicate records exist within a dataset. Integrity: Maintaining referential relationships between datasets without any broken links.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Precisely

JANUARY 9, 2023

With global data creation projected to grow to more than 180 zettabytes by 2025 , it’s not surprising that more organizations than ever are looking to harness their ever-growing datasets to drive more confident business decisions.

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Data Integration Data Governance Government Data 81

RandomTrees

FEBRUARY 6, 2024

Over the years, the field of data engineering has seen significant changes and paradigm shifts driven by the phenomenal growth of data and by major technological advances such as cloud computing, data lakes, distributed computing, containerization, serverless computing, machine learning, graph database, etc.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Ascend.io

APRIL 7, 2023

With the monolithic architectures most organizations have today, business users are stuck, constantly waiting for new data pipelines to be built or amended based on their requests. Data engineers aren’t huge fans of this paradigm either. Data fabric is far more ad hoc.

Metadata 52

Metadata Datasets Data Governance Government 52

Monte Carlo

JANUARY 16, 2024

Factor in the advertising strategies, media production, partner programming, audience analytics…and you’re looking at an ocean of data that would fill even the deepest trench (we’d like a television show about that too, please!). So how does Fox’s data strategy support these complex data workflows? Image from Castor.

Government 52

Government High Quality Data Data Governance Entertainment 52

Knowledge Hut

DECEMBER 22, 2023

Linear Algebra Linear Algebra is a mathematical subject that is very useful in data science and machine learning. A dataset is frequently represented as a matrix. Statistics Statistics are at the heart of complex machine learning algorithms in data science, identifying and converting data patterns into actionable evidence.

Hadoop 98

Hadoop Deep Learning Data Science Machine Learning 98

Databand.ai

AUGUST 30, 2023

Measuring data integrity is more complex, as it involves assessing the consistency, reliability, and trustworthiness of data throughout its lifecycle. Methods While data accuracy and data integrity have similar objectives, the methods used to achieve them are different.

Data Integration 52

Data Integration Data Cleanse Data Validation Data Governance 52

DataKitchen

APRIL 13, 2021

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Airflow — An open-source platform to programmatically author, schedule, and monitor data pipelines.

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Consulting Machine Learning Data Science Data Pipeline 120

Databand.ai

JULY 5, 2023

What Are Data Observability Tools? Data observability tools are software solutions that oversee, analyze, and improve the performance of data pipelines. Data observability tools allow teams to detect issues such as missing values, duplicate records, or inconsistent formats early on before they affect downstream processes.

Data Pipeline 52

Data Pipeline Data Lake Data Warehouse Datasets 52

Monte Carlo

JULY 11, 2023

Regardless of the approach you choose, it’s important to keep a scrutinous eye on whether or not your data outputs are matching (or close to) your expectations; often, relying on a few of these measures will do the trick. Inconsistent data: Inconsistencies within a dataset can indicate inaccuracies.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Towards Data Science

DECEMBER 4, 2023

At this stage, it’s not about ingesting data and we’ll focus more and more on business use cases. We should consider how the data will be utilised by our colleagues offering structured datasets, regularly refreshed, about: Car Performance : telemetry data is cleaned, normalised and integrated to provide a unified view.

Data Engineering 83

Data Engineering Data Engineer Engineering Data Lake 83

Towards Data Science

FEBRUARY 10, 2023

As we move firmly into the data cloud era, data leaders need metrics for the robustness and reliability of the machine–the data pipelines, systems, and engineers–just as much as the final (data) product it spits out. What level of data pipeline monitoring coverage do we need? Congratulations!

Data Pipeline 93

Data Pipeline Data Cleanse Data Machine Learning 93

Striim

NOVEMBER 15, 2023

Typically to achieve this goal various teams including data engineers, sql developers, data scientists and business analysts work independently with their own datasets and data pipelines, tools & scripts causing not only duplicate efforts but also silos of storage footprint.

Retail 52

Retail BI Data Warehouse Data Lake 52

Monte Carlo

JANUARY 12, 2024

The concept of big data – complicated datasets that are too dense for traditional computing setups to deal with – is nothing new. But what is new, or still developing at least, is the extent to which data engineers can manage, data scientists can experiment, and data analysts can analyze this treasure trove of raw business insights.

Big Data 59

Big Data Data Analytics Data Science Data Lake 59

Ascend.io

SEPTEMBER 5, 2023

Efficient Coding Over Large Datasets: Snowpark alleviates the learning curve by providing a unified layer to access data, supplemented by the DataFrame API, mitigating direct file access challenges. This enhances data governance and aids in decision-making. This paves the way for new interactions and capabilities.

IT 59

IT Scala Java Programming Language 59

Ascend.io

DECEMBER 19, 2023

That’s what we call a data pipeline. It could just as well be ‘ELT for Snowflake’ The key takeaway is that these terms are representative of the actual activity being undertaken: the construction and management of data pipelines within the Snowflake environment.

ETL Tools 52

ETL Tools IT Data Pipeline Data Warehouse 52

Striim

NOVEMBER 8, 2023

Additionally, the decentralized data storage model reduces the time to value for data consumers by eliminating the need to transport data to a central store to power analytics. Data as a product This principle can be summarized as applying product thinking to data.

Architecture 52

Architecture Generalist Government Datasets 52

Monte Carlo

MAY 2, 2023

These findings suggest data quality remains among the biggest problems facing data teams, with bad data having more severe repercussions on an organization’s revenue and data trust than in years prior. Respondents averaged 642 tables across their data lake, lakehouse, or warehouse environments.

Data Lake 80

Data Lake Data Software Engineer Software Engineering 80

Monte Carlo

JULY 13, 2023

However, to appropriately grant access to the data, data engineers need to know why the users need access to the data and when this access should be revoked. This manual data access process can lead to a bottleneck in the data pipeline , slowing down the process of accessing and utilizing data for decision-making.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Monte Carlo

OCTOBER 5, 2023

Some organizations evaluate data quality metrics based on statistical analysis , conducting a comprehensive review of data patterns and trends. Other teams use sampling techniques that evaluate a sample of a dataset and make inferences about the remaining data. This helps drastically improve time-to-detection.

Government 52

Government Data Governance Data Datasets 52

Monte Carlo

FEBRUARY 7, 2023

Metrics that can help a data quality management program. As we move firmly into the data cloud era, data leaders need metrics for the robustness and reliability of the machine–the data pipelines, systems, and engineers–just as much as the final (data) product it spits out. Congratulations!

Management 72

Management Data Data Pipeline Data Cleanse 72

Databand.ai

JULY 19, 2023

Another way data ingestion enhances data quality is by enabling data transformation. During this phase, data is standardized, normalized, and enriched. Data enrichment involves adding new, relevant information to the existing dataset, which provides more context and improves the depth and value of the data.

Data Ingestion 52

Data Ingestion Process Data Cleanse Data Governance 52

Monte Carlo

JUNE 6, 2023

While generative AI will relieve data professionals of some of their more ad hoc work, it will also give data people AI-assisted tools to more easily build, maintain, and optimize data pipelines. Either way, the outcome will be more data pipelines and more data products to be consumed by end users.

Data Engineering 84

Data Engineering Data Engineer Engineering Business Intelligence 84

Ascend.io

JULY 17, 2023

Rather, the goal is to move ownership and responsibility of the data closer to the subject matter experts who understand it, while maintaining a unified data catalog of data products that can be referenced by any other builders in the organization. Data meshes are no exception. The end result?

Datasets 52

Datasets Telecommunication Architecture Government 52

Snowflake

JUNE 29, 2023

Elasticity, Near-Zero Operations, Data Governance, and more When using Snowflake’s ML-Powered Functions, it’s easy to scale from one to millions of dimension-value combinations — it’s all possible with the elasticity and near-zero operations of Snowflake’s engine. Time Series Forecasting outputs a time-based set of predictions.

Data Science 61

Data Science Datasets Retail BI 61

AltexSoft

MARCH 14, 2023

Data transformation component in a modern data stack. Cleaning: removing or correcting inaccurate, incomplete, or irrelevant data in the dataset. Normalizing: organizing the data in a standard format to eliminate redundancy and ensure consistency. Data versioning component in a modern data stack.

IT 59

IT Data Warehouse Data Governance Data Lake 59

phData: Data Engineering

JANUARY 3, 2022

This means it’s business-critical that companies can derive value from their data to better inform business decisions, protect their enterprise and their customers, and grow their business. This comprehensive guide will cover all of the basics of data engineering including common roles, functions, and responsibilities.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

Monte Carlo

NOVEMBER 20, 2023

So, what is data quality exactly? Data quality is defined by how accurate, reliable, complete, discoverable, trustworthy and actionable a specific dataset is for a given use-case. This can make it difficult to pinpoint the root cause of data quality expectations. Below is a breakdown of what that might look like.

Government 69

Government Data Data Governance Datasets 69

Knowledge Hut

DECEMBER 26, 2023

Mining of Massive Datasets By Jure Leskovec, Anand Rajaraman, Jeff Ullma This book will provide a comprehensive understanding of large-scale data mining and network analysis. Some other features that make it a book ideal for advanced learners are as follows:- This book will help you learn how to mine significantly large datasets.

Data Science 52

Data Science Scala Programming Language R (Programming) 52