Snowflake

MARCH 16, 2023

“Ownership was difficult because we had replicas of the data everywhere, which meant we didn’t really know who to speak to about the different data sets. A lack of data standardization from disconnected processes also posed a potential risk for John Lewis. “We Governing it was overly onerous.”

Data Process 52

Data Process Cloud Process IT 52

Striim

NOVEMBER 17, 2023

Real-time data processing in the world of machine learning allows data scientists and engineers to focus on model development and monitoring. Striim’s strength lies in its capacity to connect to over 150 data sources, enabling real-time data acquisition from virtually any location and simplifying data transformations.

Machine Learning 52

Machine Learning Data Process PostgreSQL Process 52

AltexSoft

JULY 18, 2023

These seemingly unrelated terms unite within the sphere of big data, representing a processing engine that is both enduring and powerfully effective — Apache Spark. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general. GraphX is Spark’s component for processing graph data.

Big Data 52

Big Data Data Process Process Hadoop 52

Pinterest Engineering

JULY 25, 2023

Soam Acharya | Data Engineering Oversight; Keith Regier | Data Privacy Engineering Manager Background Businesses collect many different types of data. Each dataset needs to be securely stored with minimal access granted to ensure they are used appropriately and can easily be located and disposed of when necessary.

Big Data 98

Big Data Accessible Accessibility Hadoop 98

Data Engineering Podcast

NOVEMBER 27, 2022

Summary The data ecosystem has been growing rapidly, with new communities joining and bringing their preferred programming languages to the mix. This has led to inefficiencies in how data is stored, accessed, and shared across process and system boundaries.

Data Process 100

Data Process Process Metadata Business Intelligence 100

DoorDash Engineering

NOVEMBER 21, 2022

Subscribe for weekly updates The solution to real-time processing of inventory changes The simplest approach to propagating inventory level changes in the database to the rest of the system may have been to invoke the service code to take actions every time something that affects the inventory table is called.

Data Process 57

Data Process Process Kafka Database 57

Data Engineering Podcast

FEBRUARY 20, 2022

With Bigeye’s data observability platform, if there is an issue with your data or data pipelines you’ll know right away and can get it fixed before the business is impacted. Bigeye let’s data teams measure, improve, and communicate the quality of your data to company stakeholders.

Python 100

Python Data Process IT Building 100

ProjectPro

JANUARY 25, 2022

PySpark is a handy tool for data scientists since it makes the process of converting prototype models into production-ready model workflows much more effortless. PySpark is used to process real-time data with Kafka and Streaming, and this exhibits low latency. Why use PySpark? To set the master URL, use setMaster(value).

Big Data 52

Big Data Data Process Process Kafka 52

Towards Data Science

FEBRUARY 18, 2024

How to Stream and Apply Real-Time Prediction Models on High-Throughput Time-Series Data Photo by JJ Ying on Unsplash Most of the stream processing libraries are not python friendly while the majority of machine learning and data mining libraries are python based.

Kafka 76

Kafka Python Process Google Cloud 76

ThoughtSpot

OCTOBER 18, 2024

In the fast-paced world of software development, the efficiency of build processes plays a crucial role in maintaining productivity and code quality. This requirement prompted us to explore Build Analytics—harnessing data from our build processes to gain actionable insights.

Building 52

Building Process Pipeline-centric Database-centric 52

Cloudera

MARCH 2, 2023

Iceberg is a high-performance open table format for huge analytic data sets. It allows multiple data processing engines, such as Flink, NiFi, Spark, Hive, and Impala to access and analyze data in simple, familiar SQL tables. This enables you to maximize utilization of streaming data at scale.

Process 112

Process SQL Kafka Database 112

Start Data Engineering

MAY 28, 2024

Building efficient data pipelines with DuckDB 4.1. Use DuckDB to process data, not for multiple users to access data 4.2. Cost calculation: DuckDB + Ephemeral VMs = dirt cheap data processing 4.3. Processing data less than 100GB? Introduction 2. Project demo 3. Use DuckDB 4.4.

Data Pipeline 219

Data Pipeline Python Building Data 219

Knowledge Hut

JANUARY 30, 2024

ITIL Processes ITIL comprises several processes that make it extremely adaptable, scalable, and diverse. These processes consist of activities with specified inputs, causes, and outputs. Let's look at some of the ITIL Processes and ideas that underpin them. This process is completed through five successive activities.

Process 52

Process Certification Portfolio Accessibility 52

Cloudera

SEPTEMBER 11, 2018

The open data processing pipeline. IoT is expected to generate a volume and variety of data greatly exceeding what is being experienced today, requiring modernization of information infrastructure to realize value. The Enterprise Data Hub. Telemetry data routed to the Cloudera Enterprise Data Hub flows into Apache Kafka.

Data Process 40

Data Process Process Building Machine Learning 40

Start Data Engineering

OCTOBER 14, 2024

Using nested data types in data processing 3.3.1. STRUCT enables more straightforward data schema and data access 3.3.2. Nested data types can be sorted 3.3.3. Use STRUCT for one-to-one & hierarchical relationships 3.2. Use ARRAY[STRUCT] for one-to-many relationships 3.3.

SQL 130

SQL Data Schemas Data Coding 130

Confluent

MAY 7, 2019

Although it is the simplest way to subscribe to and access events from Kafka, behind the scenes, Kafka consumers handle tricky distributed systems challenges like data consistency, failover and load balancing. Data processing requirements. We therefore need a way of splitting up the data ingestion work.

Kafka 111

Kafka Accessible Accessibility Metadata 111

Knowledge Hut

JULY 24, 2023

The future of SQL (Structured Query Language) is a scalding subject among professionals in the data-driven world. As data generation continues to skyrocket, the demand for real-time decision-making, data processing, and analysis increases. It is also integrable with other programming languages like Python and R.

Database 52

Database SQL Process NoSQL 52

Knowledge Hut

JANUARY 23, 2024

To stay on top of processes and synchronize all the elements involved, I turn to one of the key best practices in the rule book: Project Integration Management. Here’s what can be achieved through integration management: Processes and tasks can be organized and listed out. Get to know more about project description.

Project 52

Project Process Management Certification 52

Cloudera

JULY 18, 2022

In this blog we will conclude the implementation of our fraud detection use case and understand how Cloudera Stream Processing makes it simple to create real-time stream processing pipelines that can achieve neck-breaking performance at scale. Data decays! It has a shelf life and as time passes its value decreases. Apache Flink.

Process 88

Process Kafka Scala SQL 88

Snowflake

JUNE 5, 2024

With Snowpark’s existing DataFrame API , users have access to a robust framework for lazily evaluated, relational operations on data, closely resembling Spark’s conventions. pandas is the go-to data processing library for millions worldwide, including countless Snowflake users.

Python 110

Python Programming Language Government SQL 110

Databand.ai

JULY 19, 2023

Complete Guide to Data Ingestion: Types, Process, and Best Practices Helen Soloveichik July 19, 2023 What Is Data Ingestion? Data Ingestion is the process of obtaining, importing, and processing data for later use or storage in a database. In this article: Why Is Data Ingestion Important?

Data Ingestion 52

Data Ingestion Process Data Cleanse Data Governance 52

Snowflake

JUNE 28, 2023

At this year’s Summit, we are excited to announce a series of advancements to Snowpark runtimes and libraries, making the deployment and processing of non-SQL code in Snowflake even simpler, faster, and more secure. Snowpark — Set of libraries and runtimes for secure deployment and processing of non-SQL code on the Snowflake Data Cloud.

Python 52

Python Accessible Accessibility Pipeline-centric 52

ProjectPro

FEBRUARY 16, 2023

At the core of such applications lies the science of machine learning, image processing, computer vision, and deep learning. As an example, consider the Facial Image Recognition System, it leverages the OpenCV Python library for implementing image processing techniques. What is OpenCV Python?

Python 98

Python Process Deep Learning Algorithm 98

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

Precisely

JULY 20, 2023

In a disruptive market, agility and speed are key to success and a competitive edge – and automating your critical SAP ® processes helps unlock those capabilities. When you set out to improve data quality and integrity, it’s critical to keep in mind the interdependence of process and data.

Process 40

Process Finance Government Coding 40

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. data import torch. But as it turns out, we can’t use it.

Medical 60

Medical Process Cloud Bytes 60

Striim

SEPTEMBER 11, 2024

With data volumes and sources rapidly increasing, optimizing how you collect, transform, and extract data is more crucial to stay competitive. That’s where real-time data, and stream processing can help. We’ll answer the question, “What are data pipelines?” Table of Contents What are Data Pipelines?

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Ascend.io

NOVEMBER 14, 2024

Why Future-Proofing Your Data Pipelines Matters Data has become the backbone of decision-making in businesses across the globe. The ability to harness and analyze data effectively can make or break a company’s competitive edge. Set Up Auto-Scaling: Configure auto-scaling for your data processing and storage resources.

Data Pipeline 52

Data Pipeline Amazon Web Services Data Integration Data 52

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

Snowflake

JUNE 4, 2024

Finally, Tasks Backfill (PrPr) automates historical data processing within Task Graphs. Additionally, Dynamic Tables are a new table type that you can use at every stage of your processing pipeline. In this initial public preview, you can access, read and write files within your Git repository.

Data Pipeline 102

Data Pipeline Python SQL Government 102

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 96

Management Kafka Metadata Media 96

Knowledge Hut

MAY 3, 2024

A new breed of ‘Fast Data’ architectures has evolved to be stream-oriented, where data is processed as it arrives, providing businesses with a competitive advantage. Dean Wampler (Renowned author of many big data technology-related books) Dean Wampler makes an important point in one of his webinars.

Kafka 98

Kafka Scala Java Amazon Web Services 98

Cloudera

DECEMBER 3, 2024

Cloudera’s open data lakehouse, powered by Apache Iceberg, solves the real-world big data challenges mentioned above by providing a unified, curated, shareable, and interoperable data lake that is accessible by a wide array of Iceberg-compatible compute engines and tools. SELECT * FROM airlines_data.carriers; 4.

Metadata 52

Metadata SQL Database Data Warehouse 52

Engineering at Meta

AUGUST 27, 2024

For example, we built Policy Zones that apply across our infrastructure to address restrictions on data, such as using it only for allowed purposes, providing strong guarantees for limiting the purposes of its processing. A crucial aspect of purpose limitation is managing data as it flows across systems and services.

Programming Language 115

Programming Language Coding Data Warehouse Systems 115

Striim

OCTOBER 11, 2024

Then, we’ll explore a data pipeline example and dive deeper into the key differences between a traditional data pipeline vs ETL. What is a Data Pipeline? A data pipeline refers to a series of processes that transport data from one or more sources to a destination, such as a data warehouse, database, or application.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

Snowflake

NOVEMBER 29, 2023

In medicine, lower sequencing costs and improved clinical access to NGS technology has been shown to increase diagnostic yield for a range of diseases, from relatively well-understood Mendelian disorders, including muscular dystrophy and epilepsy , to rare diseases such as Alagille syndrome.

Metadata 87

Metadata Healthcare Medical Data Storage 87

phData: Data Engineering

NOVEMBER 8, 2024

Open Table Format (OTF) architecture now provides a solution for efficient data storage, management, and processing while ensuring compatibility across different platforms. These systems are built on open standards and offer immense analytical and transactional processing flexibility. Why should we use it?

Architecture 52

Architecture Systems Data Lake Google Cloud 52