Netflix Tech

MARCH 10, 2023

Studio applications use this service to store their media assets, which then goes through an asset cycle of schema validation, versioning, access control, sharing, triggering configured workflows like inspection, proxy generation etc. This pattern grows over time when we need to access and update the existing assets metadata.

Management 94

Management Kafka Metadata Media 94

Snowflake

JULY 10, 2023

Announced at Summit, we’ve recently added to Snowpark the ability to process files programmatically, with Python in public preview and Java generally available. Data engineers and data scientists can take advantage of Snowflake’s fast engine with secure access to open source libraries for processing images, video, audio, and more.

Unstructured Data 40

Unstructured Data Python Process Scala 40

Snowflake

JANUARY 23, 2024

Behind the scenes, Snowpark ML parallelizes data processing operations by taking advantage of Snowflake’s scalable computing platform. This is a first-class, schema-level Snowflake object that provides a versioned container of ML model artifacts with full role-based access control (RBAC) support, and APIs for Python and SQL.

Machine Learning 120

Machine Learning Metadata Python Telecommunication 120

Knowledge Hut

MARCH 7, 2024

The year 2024 saw some enthralling changes in volume and variety of data across businesses worldwide. The surge in data generation is only going to continue. Foresighted enterprises are the ones who will be able to leverage this data for maximum profitability through data processing and handling techniques.

Big Data 98

Big Data Bytes Data Governance Raw Data 98

Rockset

MARCH 18, 2024

Flink is one of the most popular stream processing technologies, ranked as a top five Apache project and backed by a diverse committer community including Alibaba and Apple. It powers steam processing at many companies including Uber, Netflix, and Linkedin.

Cloud 64

Cloud Building Metadata Kafka 64

Striim

FEBRUARY 5, 2024

Data Fabric is a comprehensive data management approach that goes beyond traditional methods , offering a framework for seamless integration across diverse sources. By upholding data quality, organizations can trust the information they rely on for decision-making, fostering a data-driven culture built on dependable insights.

Pharmaceutical 52

Pharmaceutical Data Cleanse Metadata Medical 52

Snowflake

NOVEMBER 1, 2023

Snowflake has invested heavily in extending the Data Cloud to AI/ML workloads, starting in 2021 with the introduction of Snowpark , the set of libraries and runtimes in Snowflake that securely deploy and process Python and other popular programming languages.

Building 104

Building Python SQL Programming Language 104

Towards Data Science

NOVEMBER 4, 2023

The data engineering landscape is constantly changing but major trends seem to remain the same. How to Become a Data Engineer As a data engineer, I am tasked to design efficient data processes almost every day. It was created by Spotify to manage massive data processing workloads.

Data Engineering 79

Data Engineering Data Engineer Engineering BI 79

RandomTrees

FEBRUARY 6, 2024

Modernization in Data Engineering with GenAI Generation: The Art of Data Creation: Generative AI has emerged as a potent tool for creating synthetic datasets. Generative AI corrects data imbalances, ensuring fair sentiment analysis on e-commerce platforms, enriches training data for natural language processing (NLP) tasks.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Tweag

APRIL 19, 2023

The MedTech industry is buzzing thanks to a continuous stream of innovation, promising to be more precise, efficient and accessible than ever. To allow innovation in medical imaging with AI, we need efficient and affordable ways to store and process these WSIs at scale. But as it turns out, we can’t use it. _slides_specs. width , spec.

Medical 59

Medical Process Cloud Bytes 59

ProjectPro

FEBRUARY 8, 2023

In 2023, more than 5140 businesses worldwide have started using AWS Glue as a big data tool. For e.g., Finaccel, a leading tech company in Indonesia, leverages AWS Glue to easily load, process, and transform their enterprise data for further processing. AWS Glue automates several processes as well.

AWS 98

AWS Scala Metadata Data Lake 98

Data Engineering Podcast

AUGUST 28, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. RudderStack helps you build a customer data platform on your warehouse or data lake.

Data Engineering 100

Data Engineering Data Engineer MongoDB Metadata 100

Netflix Tech

DECEMBER 3, 2022

Obviously not all tools are made with the same use case in mind, so we are planning to add more code samples for other (than classical batch ETL) data processing purposes, e.g. Machine Learning model building and scoring. The main workflow definition file holds the logic of a single run, in this case one day-worth of data.

Data Pipeline 143

Data Pipeline Scala Metadata Food 143

The Modern Data Company

APRIL 15, 2024

The Role of a Data Model Explained Think of a data model as the ultimate organizer in the vast library of your company’s data. Its job, from its position near the end of the data processing line, is similar to that of a librarian who: Answers queries from various departments looking for specific insights.

Government 52

Government Metadata Data Data Lake 52

Databand.ai

AUGUST 30, 2023

Data quality monitoring refers to the assessment, measurement, and management of an organization’s data in terms of accuracy, consistency, and reliability. It utilizes various techniques to identify and resolve data quality issues, ensuring that high-quality data is used for business processes and decision-making.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

LinkedIn Engineering

JULY 19, 2023

Open source data lakehouse deployments are built on the foundations of compute engines (like Apache Spark, Trino, Apache Flink), distributed storage (HDFS, cloud blob stores), and metadata catalogs / table formats (like Apache Iceberg, Delta, Hudi, Apache Hive Metastore). Tables are governed as per agreed upon company standards.

Big Data 78

Big Data Data Management Management Metadata 78

Data Engineering Podcast

JANUARY 30, 2022

Matt Harrison is a Python expert with a long history of working with data who now spends his time on consulting and training. You can observe your pipelines with built in metadata search and column level lineage. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders.

Data Engineering 100

Data Engineering Data Engineer Engineering Data Pipeline 100

AltexSoft

DECEMBER 23, 2022

Integrating data from numerous, disjointed sources and processing it to provide context provides both opportunities and challenges. One of the ways to overcome challenges and gain more opportunities in terms of data integration is to build an ELT (Extract, Load, Transform) pipeline. Order of process phases. What is ELT?

Process 52

Process Building Raw Data Data Lake 52

Ascend.io

FEBRUARY 28, 2024

Engineers ensure the availability of clean, structured data, a necessity for AI systems to learn from patterns, make accurate predictions, and automate decision-making processes. Through the design and maintenance of efficient data pipelines , data engineers facilitate the seamless flow and accessibility of data for AI processing.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Cloudyard

APRIL 4, 2024

However, the volume of daily transaction data poses challenges in effectively segmenting customers and optimizing engagement. This blog post explores how Snowpark, a powerful tool for data processing within Snowflake, can be used to perform RFM segmentation and unlock actionable customer insights.

Retail 40

Retail Data Ingestion Metadata Datasets 40

Monte Carlo

FEBRUARY 12, 2024

To differentiate and expand the usefulness of these models, organizations must augment them with first-party data – typically via a process called RAG (retrieval augmented generation). Today, this first-party data mostly lives in two types of data repositories. Quality : Is the data itself anomalous?

Database 72

Database Unstructured Data Data Pipeline Metadata 72

Cloudera

MARCH 3, 2022

Data analytics and machine learning can become a business and a compliance risk if data security, governance, lineage, metadata management, and automation are not holistically applied across the entire data lifecycle and all environments. Afterall, retrofitting good governance is a momentous task.

Data Governance 100

Data Governance Government Metadata Medical 100

Ascend.io

APRIL 19, 2023

The “modern data stack” has become increasingly prominent in recent years, promising a streamlined approach to data processing. This broader, “upstack-oriented” audience demanded more accessible, consumer-grade data products, which in turn led to the development of the modern data stack as we know it.

Metadata 52

Metadata Business Analyst Hadoop Software Engineer 52

AltexSoft

AUGUST 22, 2022

Some would say that it’s not a big deal, however, these mixed environments have resulted in the complexities of managing disjointed data and business processes. With these challenges in enterprise data management, there has to be an approach to overcoming them, right? Data fabric architecture example. Unified data access.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Databand.ai

JULY 19, 2023

Ensuring data quality and accuracy: Data lineage tools help ensure data quality and accuracy by providing a detailed view of the data’s journey. This allows businesses to identify any transformations or processes that may be compromising the data’s integrity.

Pipeline-centric 52

Pipeline-centric Data Governance Metadata Government 52

AltexSoft

JUNE 7, 2021

Hadoop and Spark are the two most popular platforms for Big Data processing. They both enable you to deal with huge collections of data no matter its format — from Excel tables to user feedback on websites to images and video files. Obviously, Big Data processing involves hundreds of computing units. scalability.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Monte Carlo

MAY 30, 2023

What makes Iceberg tables so appealing is they can store raw data at scale to support typical data lake use cases, but they also have data lakehouse-like properties as well such as well-organized metadata, ACID transactions, and critical features like time travel.

Metadata 52

Metadata Raw Data Data Lake Data 52

Engineering at Meta

FEBRUARY 20, 2024

Why we need a composable data management system Meta’s data engines support large-scale workloads that include processing large datasets offline (ETL), interactive dashboard generation, ad hoc data exploration, and stream processing. first writing StringView at position 2, then 0 and 1).

Data Management 103

Data Management Bytes Management Datasets 103

Monte Carlo

FEBRUARY 21, 2023

In this post, we’ll discuss what, exactly, a data fabric is, how other companies have used it, and how you can build one at your company. Table of Contents What is a data fabric? A data fabric offers unity in a formerly disconnected, incompatible data environment. Multiple benefits characterize the data fabric.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Monte Carlo

FEBRUARY 21, 2023

In this post, we’ll discuss what, exactly, a data fabric is, how other companies have used it, and how you can build one at your company. Table of Contents What is a data fabric? A data fabric offers unity in a formerly disconnected, incompatible data environment. Multiple benefits characterize the data fabric.

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Monte Carlo

APRIL 24, 2023

Traditionally, after being stored in a data lake, raw data was then often moved to various destinations like a data warehouse for further processing, analysis, and consumption. Databricks Data Catalog and AWS Lake Formation are examples in this vein. AWS is one of the most popular data lake vendors.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

AltexSoft

FEBRUARY 11, 2023

What’s more, investing in data products, as well as in AI and machine learning was clearly indicated as a priority. This suggests that today, there are many companies that face the need to make their data easily accessible, cleaned up, and regularly updated. This privacy law must be kept in mind when building data architecture.

Data Architect 87

Data Architect Certification Generalist Big Data 87

AltexSoft

AUGUST 29, 2023

Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture. This structure is made efficient by data engineering practices that include object storage. Watch our video explaining how data engineering works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Cloudera

MARCH 14, 2023

Developers who are tasked with building these data pipelines are looking for tooling that: Gives them a development environment on demand without having to maintain it. Allows them to iteratively develop processing logic and test with as little overhead as possible.

Data Pipeline 84

Data Pipeline Designing Kafka Metadata 84

Monte Carlo

AUGUST 25, 2023

So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform? Understanding data warehouses A data warehouse is a consolidated storage unit and processing hub for your data. Let’s dive in.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Towards Data Science

MARCH 5, 2024

In that case, queries are still processed using the BigQuery compute infrastructure but read data from GCS instead. Such external tables come with some disadvantages but in some cases it can be more cost efficient to have the data stored in GCS. BigQuery Studio If it says 1.27

Bytes 67

Bytes Google Cloud Cloud Storage Utilities 67

Ascend.io

FEBRUARY 15, 2024

They play a pivotal role in ensuring data consistency, accuracy, and accessibility because they don’t just clone your data once; it’s an ongoing strategy to keep multiple, identical copies of your data in sync across various sources. Why are Data Replication Strategies Important?

Datasets 52

Datasets Database Data Warehouse Data 52