Data Engineering Weekly

MAY 16, 2023

In the second part, we will focus on architectural patterns to implement data quality from a data contract perspective. Why is Data Quality Expensive? I won’t bore you with the importance of data quality in the blog. Let’s talk about the data processing types.

Engineering 110

Engineering Kafka Data Pipeline Data Warehouse 110

LinkedIn Engineering

DECEMBER 12, 2023

Pinot is a columnar OLAP store that serves analytics queries on data ingested from realtime streams. PEDAL also consists of a metadata store that holds various algorithmic parameters, including the scale of noise that we introduce and whether we should use one-shot or continual observation algorithms.

Algorithm 77

Algorithm Metadata SQL Datasets 77

DataKitchen

OCTOBER 20, 2023

The Challenge: High Stakes in the Age of Personalized Data Observability The primary challenge stems from the requirement of Data Consumers for personalized monitoring and alerts based on their unique data processing needs. Data Observability platforms often need to deliver this level of customization.

Insurance 70

Insurance Pharmaceutical Data Data Ingestion 70

Netflix Tech

OCTOBER 19, 2020

In our previous blog post we introduced Edgar, our troubleshooting tool for streaming sessions. Reconstructing a streaming session was a tedious and time consuming process that involved tracing all interactions (requests) between the Netflix app, our Content Delivery Network (CDN), and backend microservices.

Building 93

Building Transportation Metadata Java 93

RandomTrees

SEPTEMBER 6, 2020

This stage handles all the aspects of data storage like organization, file size, structure, compression, metadata, statistics. The data objects are accessible only through SQL query operations run using Snowflake. Query Processing: Query processing in Snowflake is done using virtual warehouses.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Cloudera

SEPTEMBER 28, 2020

The data lifecycle model ingests data using Kafka, enriches that data with Spark-based batch process, performs deep data analytics using Hive and Impala, and finally uses that data for data science using Cloudera Data Science Workbench to get deep insights. Phase 1: Planning.

Cloud 131

Cloud Kafka Professional Services Metadata 131

Cloudera

FEBRUARY 9, 2021

Today’s customers have a growing need for a faster end to end data ingestion to meet the expected speed of insights and overall business demand. This ‘need for speed’ drives a rethink on building a more modern data warehouse solution, one that balances speed with platform cost management, performance, and reliability.

Data Warehouse 69

Data Warehouse Cloud Kafka Cloud Storage 69

Cloudera

AUGUST 31, 2021

Running on CDW is fully integrated with streaming, data engineering, and machine learning analytics. It has a consistent framework that secures and provides governance for all data and metadata on private clouds, multiple public clouds, or hybrid clouds. Consideration of both data & metadata in the migration.

Data Warehouse 80

Data Warehouse Database-centric Metadata Cloud 80

Maxime Beauchemin

JANUARY 20, 2017

This discipline also integrates specialization around the operation of so called “big data” distributed systems, along with concepts around the extended Hadoop ecosystem, stream processing, and in computation at scale. Sure, there’s a need to abstract the complexity of data processing, computation and storage.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Databand.ai

JUNE 28, 2023

It goes beyond basic monitoring to provide a deeper understanding of how data is moving and being transformed in a pipeline, and is often associated with metrics, logging, and tracing data pipelines. Data pipelines often involve a series of stages where data is collected, transformed, and stored.

Data Pipeline 98

Data Pipeline Data Engineering Data Engineer Engineering 98

Cloudera

FEBRUARY 16, 2022

Weak model lineage can result in reduced model performance, a lack of confidence in model predictions and potentially violation of company, industry or legal regulations on how data is used. . Within the CML data service, model lineage is managed and tracked at a project level by the SDX. Figure 02: ML Model Lineage with SDX.

Machine Learning 81

Machine Learning Algorithm Government Metadata 81

Knowledge Hut

SEPTEMBER 29, 2023

Moreover, what benefits can you expect from a career in Azure Data Engineering? This blog aims to answer these questions, providing a straightforward and professional insight into the world of Azure Data Engineering. Join us on this journey through the exciting realm of Azure Data Engineering.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

LinkedIn Engineering

JUNE 15, 2023

However, we found that many of our workloads were bottlenecked by reading multiple terabytes of input data. To remove this bottleneck, we built AvroTensorDataset , a TensorFlow dataset for reading, parsing, and processing Avro data. If greater than one, records in files are processed in parallel.

Datasets 102

Datasets Bytes Process Data Ingestion 102

Databand.ai

AUGUST 30, 2023

DataOps , short for data operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data processes across an organization. By using DataOps tools, organizations can break down silos, reduce time-to-insight, and improve the overall quality of their data analytics processes.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Cloudera

DECEMBER 15, 2022

Since we announced the general availability of Apache Iceberg in Cloudera Data Platform (CDP), Cloudera customers, such as Teranet , have built open lakehouses to future-proof their data platforms for all their analytical workloads. Only metadata will be regenerated. Data quality using table rollback.

Cloud 76

Cloud Metadata Google Cloud Data Warehouse 76

Cloudyard

MARCH 4, 2023

DCDW Architecture Above all, Architecture was divided into three Business layers: Firstly,Agile Data ingestion : Heterogeneous Source System fed the data into Cloud. Respective Cloud would consume/Store the data in bucket or containers. Load the data AS-IS into Snowflake called RAW layer.

Architecture 52

Architecture Cloud Metadata Data Ingestion 52

Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. The script will go through loading RAPIDs libraries then leveraging them to load and processing a datafile. Data Ingestion. The raw data is in a series of CSV files.

Machine Learning 136

Machine Learning Datasets Data Science Raw Data 136

Netflix Tech

DECEMBER 21, 2020

There are several benefits of such optimizations like saving on storage, faster query time, cheaper downstream processing, and an increase in developer productivity by removing additional ETLs written only for query performance improvement. This enables us to add additional indexes in the metadata to make point queries more optimal.

Data Warehouse 81

Data Warehouse Metadata Algorithm Data 81

Cloudera

DECEMBER 20, 2021

Understanding that the future of banking is data-driven and cloud-based, Bank of the West embraced cloud computing and its benefits, like remote capabilities, integrated processes, and flexible systems. Winner of the Data Impact Awards 2021: Security & Governance Leadership. You can become a data hero too.

Government 86

Government Data Security Banking Metadata 86

ProjectPro

FEBRUARY 8, 2023

Do ETL and data integration activities seem complex to you? Read this blog to understand everything about AWS Glue that makes it one of the most popular data integration solutions in the industry. Did you know the global big data market will likely reach $268.4 Businesses are leveraging big data now more than ever.

AWS 98

AWS Scala Metadata Data Lake 98

Netflix Tech

MARCH 25, 2019

We adopted the following mission statement to guide our investments: “Provide a complete and accurate data lineage system enabling decision-makers to win moments of truth.” Therefore, the ingestion approach for data lineage is designed to work with many disparate data sources. push or pull.

Building 87

Building Metadata Transportation Data Ingestion 87

Cloudera

FEBRUARY 8, 2021

This is part 2 in this blog series. You can read part 1, here: Digital Transformation is a Data Journey From Edge to Insight. The first blog introduced a mock connected vehicle manufacturing company, The Electric Car Company (ECC), to illustrate the manufacturing data path through the data lifecycle.

Data Pipeline 114

Data Pipeline Building Manufacturing Data Warehouse 114

Cloudera

MARCH 31, 2021

Customers who have chosen Google Cloud as their cloud platform can now use CDP Public Cloud to create secure governed data lakes in their own cloud accounts and deliver security, compliance and metadata management across multiple compute clusters. Data Preparation (Apache Spark and Apache Hive) .

Google Cloud 90

Google Cloud Cloud Amazon Web Services Cloud Storage 90

LinkedIn Engineering

JULY 27, 2023

However, the ease of these processes can lead to over-provisioning and under-utilization of cloud resources, resulting in increased operating expenses. In this blog post, we will share our progress, challenges, and lessons learned from our Costwiz journey.

Metadata 75

Metadata Utilities Cloud Data Lake 75

Cloudera

SEPTEMBER 9, 2022

With this in mind, it’s clear that no “one size fits all” architecture will work here; we need a diverse set of data services, fit for each workload and purpose, backed by optimized compute engines and tools. . Data changes in numerous ways: the shape and form of the data changes; the volume, variety, and velocity changes.

Architecture 97

Architecture Metadata Unstructured Data Machine Learning 97

DataKitchen

SEPTEMBER 21, 2023

L1 is usually the raw, unprocessed data ingested directly from various sources; L2 is an intermediate layer featuring data that has undergone some form of transformation or cleaning; and L3 contains highly processed, optimized, and typically ready for analytics and decision-making processes.

Raw Data 70

Raw Data Data Business Intelligence High Quality Data 70

Rockset

JUNE 6, 2023

In this blog, we’ll walk through how Rockset provides compute-storage separation while making real-time data available to queries. Virtual instances (VIs) are allocations of compute and memory resources responsible for data ingestion, transformations, and queries.

Metadata 52

Metadata Datasets Architecture Algorithm 52

Ascend.io

JUNE 8, 2023

However, transforming data into a product so that it can deliver outsized business value requires more than just a mission statement; it requires a solid foundation of technical capabilities and a truly data-centric culture. This multitude of sources often causes a dispersed, complex, and poorly structured data landscape.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Cloudera

JUNE 30, 2022

With Cloudera’s vision of hybrid data , enterprises adopting an open data lakehouse can easily get application interoperability and portability to and from on premises environments and any public cloud without worrying about data scaling. Why integrate Apache Iceberg with Cloudera Data Platform?

Data Lake 84

Data Lake Business Intelligence Metadata Data Warehouse 84

ProjectPro

AUGUST 24, 2021

Data professionals who work with raw data like data engineers, data analysts, machine learning scientists , and machine learning engineers also play a crucial role in any data science project. And, out of these professions, this blog will discuss the data engineering job role.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Snowflake

MAY 22, 2023

Cross-Cloud Snowgrid Account Replication expands replication beyond databases – general availability Account Replication, now generally available, expands replication beyond databases to account metadata and integrations, making business continuity truly turnkey. Read our announcement blog post for more.

Healthcare 52

Healthcare Scala Medical Transportation 52

Netflix Tech

DECEMBER 14, 2018

In the previous blog posts in this series, we introduced the N etflix M edia D ata B ase ( NMDB ) and its salient “Media Document” data model. A fundamental requirement for any lasting data system is that it should scale along with the growth of the business applications it wishes to serve.

Media 94

Media Database Metadata Data Schemas 94

Snowflake

MARCH 30, 2023

Collecting, cleaning, and organizing data into a coherent form for business users to consume are all standard data modeling and data engineering tasks for loading a data warehouse. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

Engineering 82

Engineering Raw Data Data Science Scala 82

Cloudera

JUNE 4, 2018

The solution to this massive data challenge embedded the Aspire Content Processing Framework into the Cloudera Enterprise Data Hub as a Cloudera Parcel – a binary distribution format containing the program files, along with additional metadata used by Cloudera Manager.

Pharmaceutical 45

Pharmaceutical Unstructured Data Electronics Metadata 45

ProjectPro

JANUARY 31, 2023

If you're looking to break into the exciting field of big data or advance your big data career, being well-prepared for big data interview questions is essential. Get ready to expand your knowledge and take your big data career to the next level! But the concern is - how do you become a big data professional?

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

NOVEMBER 15, 2021

Table of Contents 20 Open Source Big Data Projects To Contribute How to Contribute to Open Source Big Data Projects? 20 Open Source Big Data Projects To Contribute There are thousands of open-source projects in action today. This blog will walk through the most popular and fascinating open source big data projects.

Big Data 52

Big Data Project Metadata Programming Language 52

Cloudera

AUGUST 21, 2020

And next to those legacy ERP, HCM, SCM and CRM systems, that mysterious elephant in the room – that “Big Data” platform running in the data center that is driving much of the company’s analytics and BI – looks like a great potential candidate. . Streaming data analytics. . Data science & engineering.

Hadoop 73

Hadoop Big Data Cloud Kafka 73