Ascend.io

MARCH 17, 2023

These engineering functions are almost exclusively concerned with data pipelines, spanning ingestion, transformation, orchestration, and observation — all the way to data product delivery to the business tools and downstream applications. Pipelines need to grow faster than the cost to run them.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineer 52

Precisely

MAY 13, 2024

A true enterprise-grade integration solution calls for source and target connectors that can accommodate: VSAM files COBOL copybooks open standards like JSON modern platforms like Amazon Web Services ( AWS ), Confluent , Databricks , or Snowflake Questions to ask each vendor: Which enterprise data sources and targets do you support?

Data Integration 52

Data Integration Metadata Amazon Web Services Data Governance 52

Data Engineering Podcast

NOVEMBER 20, 2022

Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. What is the workflow for someone getting Sifflet integrated into their data stack?

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Data Lake Data Ingestion MongoDB Scala 130

Christophe Blefari

JANUARY 20, 2024

Then here a list of global resources that can help you navigate through the field: The Data Engineer Roadmap — An image with advices and technology names to watch. Reddit r/dataengineering wiki a place where some data eng definitions are written. workflows (Airflow, Prefect, Dagster, etc.) Is it really modern?

Data Engineering 130

Data Engineering Data Engineer Engineering Hadoop 130

Netflix Tech

JANUARY 25, 2023

Scalable Annotation Service — Marken by Varun Sekhri , Meenakshi Jindal Introduction At Netflix, we have hundreds of micro services each with its own data models or entities. For example, we have a service that stores a movie entity’s metadata or a service that stores metadata about images. In this case it is BOUNDING_BOX.

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Algorithm Media Metadata Data Ingestion 113

Towards Data Science

NOVEMBER 4, 2023

I’d like to discuss some popular Data engineering questions: Modern data engineering (DE). Does your DE work well enough to fuel advanced data pipelines and Business intelligence (BI)? Are your data pipelines efficient? and parallel data processing. What is it? ML model training using Airflow.

Data Engineering 86

Data Engineering Data Engineer Engineering BI 86

Ascend.io

FEBRUARY 6, 2023

Data Flow – is an individual data pipeline. Data Flows include the ingestion of raw data, transformation via SQL and python, and sharing of finished data products. Data Plane – is the data cloud where the data pipeline workload runs, like Databricks, BigQuery, and Snowflake.

Cloud 52

Cloud Data Ingestion Raw Data Data Pipeline 52

Ascend.io

FEBRUARY 6, 2023

Data Flow – is an individual data pipeline. Data Flows include the ingestion of raw data, transformation via SQL and python, and sharing of finished data products. Data Plane – is the data cloud where the data pipeline workload runs, like Databricks, BigQuery, and Snowflake.

Cloud 52

Cloud Data Ingestion Raw Data Data Pipeline 52

Data Engineering Weekly

MAY 16, 2023

I won’t bore you with the importance of data quality in the blog. Instead, Let’s examine the current data pipeline architecture and ask why data quality is expensive. Instead of looking at the implementation of the data quality frameworks, Let's examine the architectural patterns of the data pipeline.

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Engineering Kafka Data Pipeline Data Warehouse 110

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Data Engineering Podcast

JUNE 5, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking all of that information into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Go to dataengineeringpodcast.com/atlan today to learn more about how you can take advantage of active metadata and escape the chaos.

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Data Security Metadata MongoDB Scala 100

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. These tools help organizations implement DataOps practices by providing a unified platform for data teams to collaborate, share, and manage their data assets.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Monte Carlo

APRIL 4, 2023

That’s why, in addition to integrating with your central data warehouse , lake , and lakehouse , Monte Carlo also integrates with transformation , orchestration , and now data ingestion tools. Now teams can instantly get full visibility into how these systems may be impacting their data assets, all in a single pane of glass.

BI 64

BI Data Ingestion Metadata Data Pipeline 64

Ascend.io

JUNE 8, 2023

The Essential Six Capabilities To set the stage for impactful and trustworthy data products in your organization, you need to invest in six foundational capabilities. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

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Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

DataKitchen

OCTOBER 20, 2023

The Solution: ‘Payload’ Data Journeys Traditional Data Observability usually focuses on a ‘process journey,’ tracking the performance and status of data pipelines. ’ It assigns unique identifiers to each data item—referred to as ‘payloads’—related to each event.

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Insurance Pharmaceutical Data Data Ingestion 67

Monte Carlo

MARCH 28, 2023

The most prominent, recent examples are Snowflake and Databricks disrupting the concept of the database and ushering in the modern data stack era. As part of this movement, Fivetran and dbt fundamentally altered the data pipeline from ETL to ELT. Zero-ETL What it is : A misnomer for one thing; the data pipeline still exists.

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Data Warehouse Raw Data Data Pipeline Software Engineer 126

Monte Carlo

NOVEMBER 14, 2023

Databricks announced that Delta tables metadata will also be compatible with the Iceberg format, and Snowflake has also been moving aggressively to integrate with Iceberg. How Apache Iceberg tables structure metadata. I think it’s safe to say it’s getting pretty cold in here. Image courtesy of Dremio. So, is Iceberg right for you?

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Data Lake Metadata Data Warehouse SQL 64

Data Engineering Weekly

OCTOBER 30, 2022

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. Sign up free to test out the tool today.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

Booking.com Engineering

DECEMBER 2, 2022

From data ingestion, data science, to our ad bidding[2], GCP is an accelerant in our development cycle, sometimes reducing time-to-market from months to weeks. Data Ingestion and Analytics at Scale Ingestion of performance data, whether generated by a search provider or internally, is a key input for our algorithms.

Systems 52

Systems Cloud MySQL Relational Database 52

Data Engineering Podcast

AUGUST 6, 2022

Data stacks are becoming more and more complex. This brings infinite possibilities for data pipelines to break and a host of other issues, severely deteriorating the quality of the data and causing teams to lose trust. What are some of the data management considerations that are introduced by vector databases?

Machine Learning 100

Machine Learning Database MySQL PostgreSQL 100

Rockset

AUGUST 28, 2023

Query across your ANN indexes on vector embeddings, and your JSON and geospatial “metadata” fields efficiently. Spin a Virtual Instance for streaming data ingestion. If you know SQL, you already know how to use Rockset. We obsess about efficiency in the cloud. Spin another completely isolated Virtual Instance for your app.

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Metadata Unstructured Data SQL Database 78

Ascend.io

JUNE 21, 2023

In the case of data products, these are networks of data pipelines, which makes them an integral part of your modern operational machinery. To determine the ROI of any particular data product, you need to attribute the costs of building and running data pipelines.

Pipeline-centric 52

Pipeline-centric Data Pipeline Metadata Data 52

Towards Data Science

APRIL 3, 2023

The most prominent, recent examples are Snowflake and Databricks disrupting the concept of the database and ushering in the modern data stack era. As part of this movement, Fivetran and dbt fundamentally altered the data pipeline from ETL to ELT. Zero-ETL What it is : A misnomer for one thing; the data pipeline still exists.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Warehouse 98

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

RandomTrees

SEPTEMBER 6, 2020

The architecture is three layered: Database Storage: Snowflake has a mechanism to reorganize the data into its internal optimized, compressed and columnar format and stores this optimized data in cloud storage. This stage handles all the aspects of data storage like organization, file size, structure, compression, metadata, statistics.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

ProjectPro

FEBRUARY 25, 2022

Efficient Scheduling and Runtime Increased Adaptability and Scope Faster Analysis and Real-Time Prediction Introduction to the Machine Learning Pipeline Architecture How to Build an End-to-End a Machine Learning Pipeline? Is python suitable for machine learning pipeline design patterns?

Machine Learning 52

Machine Learning Building Amazon Web Services AWS 52

Netflix Tech

MARCH 25, 2019

You are about to make structural changes to the data and want to know who and what downstream to your service will be impacted. Finally, imagine yourself in the role of a data platform reliability engineer tasked with providing advanced lead time to data pipeline (ETL) owners by proactively identifying issues upstream to their ETL jobs.

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Building Metadata Transportation Data Ingestion 87

Ascend.io

MAY 19, 2023

In our case, data ingestion, transformation, orchestration, reverse ETL, and observability. This is the modern data stack as we know it today. The modern data stack has become disjointed and complex, slowing data engineering’s productivity and limiting their ability to provide value to the business.

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Media 52

Netflix Tech

DECEMBER 21, 2020

We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits. Sometimes Data Engineers write downstream ETLs on ingested data to optimize the data/metadata layouts to make other ETL processes cheaper and faster.

Data Warehouse 81

Data Warehouse Metadata Algorithm Data 81

Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? Databricks – Databricks, the Apache Spark-as-a-service platform, has pioneered the data lakehouse, giving users the options to leverage both structured and unstructured data and offers the low-cost storage features of a data lake. Image courtesy of Monte Carlo.

Building 52

Building BI Data Lake Data Governance 52

Silectis

JANUARY 3, 2021

Data Catalog An organized inventory of data assets relying on metadata to help with data management. Data Engineering Data engineering is a process by which data engineers make data useful. Data Integration Combining data from various, disparate sources into one unified view.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Cloudera

SEPTEMBER 28, 2020

Cloudera delivers an enterprise data cloud that enables companies to build end-to-end data pipelines for hybrid cloud, spanning edge devices to public or private cloud, with integrated security and governance underpinning it to protect customers data. Data Science and machine learning workloads using CDSW.

Cloud 131

Cloud Kafka Professional Services Metadata 131

Rockset

NOVEMBER 28, 2022

First, CDC theoretically allows companies to analyze and react to data in real time, as it’s generated. It works with existing streaming systems like Apache Kafka, Amazon Kinesis, and Azure Events Hubs, making it easier than ever to build a real-time data pipeline. Striim : A unified data integration and streaming platform.

Data Warehouse 52

Data Warehouse PostgreSQL MongoDB Data Pipeline 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. Visit our documentation page to learn more.

Healthcare 52

Healthcare Scala Medical Transportation 52

ProjectPro

NOVEMBER 15, 2021

To execute pipelines, beam supports numerous distributed processing back-ends, including Apache Flink, Apache Spark , Apache Samza, Hazelcast Jet, Google Cloud Dataflow, etc. Apache Airflow Source: airflow.apache.org Apache Airflow is a programming-based framework for automating authoring, scheduling, and monitoring Beam data pipelines.

Big Data 52

Big Data Project Metadata Programming Language 52

AltexSoft

MARCH 30, 2023

Databricks architecture Databricks provides an ecosystem of tools and services covering the entire analytics process — from data ingestion to training and deploying machine learning models. Besides that, it’s fully compatible with various data ingestion and ETL tools. Let’s see what exactly Databricks has to offer.

Scala 64

Scala Data Lake BI Google Cloud 64

ProjectPro

AUGUST 24, 2021

Data Sourcing: Building pipelines to source data from different company data warehouses is fundamental to the responsibilities of a data engineer. So, work on projects that guide you on how to build end-to-end ETL/ELT data pipelines. Google BigQuery receives the structured data from workers.

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Data Engineering Data Engineer Coding Project 52