Monte Carlo

NOVEMBER 22, 2024

Let’s walk through how to transform your scrappy data setup into a robust pipeline that’s ready to grow with your business. At the front end, you’ve got your data ingestion layer —the workhorse that pulls in data from everywhere it lives.

Data Engineering 52

Data Engineering Data Engineer Building Engineering 52

Rockset

MARCH 1, 2023

Rockset introduces a new architecture that enables separate virtual instances to isolate streaming ingestion from queries and one application from another. Benefits of Compute-Compute Separation In this new architecture, virtual instances contain the compute and memory needed for streaming ingest and queries.

Architecture 59

Architecture AWS SQL Cloud Storage 59

Data Engineering Podcast

JUNE 12, 2022

The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java.

Unstructured Data 130

Unstructured Data MongoDB Scala MySQL 130

ProjectPro

FEBRUARY 6, 2023

Application Integration With data exchange capabilities and event notifications across different systems and platforms, it can be a good choice for integrating applications with each other particularly when there are multiple applications and services where data needs to be shared.

Google Cloud 52

Google Cloud Cloud Cloud Storage Data Ingestion 52

ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. As data is expanding exponentially, organizations struggle to harness digital information's power for different business use cases. What is a Big Data Pipeline?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Cloudera

MARCH 31, 2021

CDP Public Cloud is already available on Amazon Web Services and Microsoft Azure. With the addition of Google Cloud, we deliver on our vision of providing a hybrid and multi-cloud architecture to support our customer’s analytics needs regardless of deployment platform. . Data Preparation (Apache Spark and Apache Hive) .

Google Cloud 93

Google Cloud Cloud Amazon Web Services Cloud Storage 93

RandomTrees

SEPTEMBER 6, 2020

Lot of cloud-based data warehouses are available in the market today, out of which let us focus on Snowflake. Snowflake is an analytical data warehouse that is provided as Software-as-a-Service (SaaS). Built on new SQL database engine, it provides a unique architecture designed for the cloud.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

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 72

Data Warehouse Cloud Kafka Cloud Storage 72

phData: Data Engineering

AUGUST 4, 2023

Data storage is a vital aspect of any Snowflake Data Cloud database. Within Snowflake, data can either be stored locally or accessed from other cloud storage systems. In Snowflake, there are three different storage layers available, Database, Stage, and Cloud Storage.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

Towards Data Science

OCTOBER 9, 2024

Get started with Airbyte and Cloud Storage Coding the connectors yourself? Think very carefully Creating and maintaining a data platform is a hard challenge. Not only do you have to make it scalable and useful, but every architectural decision builds up over time. Data connectors are an essential part of such a platform.

Coding 53

Coding Cloud Storage Data Data Ingestion 53

Towards Data Science

MARCH 5, 2024

In that case, queries are still processed using the BigQuery compute infrastructure but read data from GCS instead. In this environment, the emphasis shifts from minimizing storage space to optimizing query performance. Load data For data ingestion Google Cloud Storage is a pragmatic way to solve the task.

Bytes 69

Bytes Google Cloud Cloud Storage Utilities 69

Knowledge Hut

SEPTEMBER 26, 2023

Born out of the minds behind Apache Spark, an open-source distributed computing framework, Databricks is designed to simplify and accelerate data processing, data engineering, machine learning, and collaborative analytics tasks. This flexibility allows organizations to ingest data from virtually anywhere.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 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

ProjectPro

AUGUST 24, 2021

If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of best data engineering project examples below. With the trending advance of IoT in every facet of life, technology has enabled us to handle a large amount of data ingested with high velocity.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Rockset

MARCH 5, 2021

Understanding the space-time tradeoff in data analytics In computer science, a space-time tradeoff is a way of solving a problem or calculation in less time by using more storage space, or by solving a problem in very little space by spending a long time. However for each query it needs to scan your data.

Cloud Storage 52

Cloud Storage Data Ingestion Data Warehouse Computer Science 52

WeCloudData

OCTOBER 19, 2021

The team was able to achieve this by leveraging cloud as well as open source tools in a modular set up, taking advantage of relatively cheap cloud storage, a versatile programming language in Python and Spark’s powerful processing engine.

Consulting 52

Consulting Raw Data Data Lake Data Pipeline 52

WeCloudData

OCTOBER 19, 2021

The team was able to achieve this by leveraging cloud as well as open source tools in a modular set up, taking advantage of relatively cheap cloud storage, a versatile programming language in Python and Spark’s powerful processing engine.

Consulting 52

Consulting Raw Data Data Lake Data Pipeline 52

Confluent

FEBRUARY 6, 2019

It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way. This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers. So how can the Kafka ecosystem help here?

Machine Learning 107

Machine Learning Python Kafka Java 107

Rockset

MARCH 15, 2021

While there’s typically some amount of data engineering required here, there are ways to minimize it. For example, instead of denormalizing the data, you could use a query engine that supports joins. This will avoid unnecessary processing during data ingestion and reduce the storage bloat due to redundant data.

MongoDB 40

MongoDB Data Ingestion Analytics Application Kafka 40

Knowledge Hut

JULY 6, 2023

However, there are costs associated with data ingestion. Cloud Combine Another Azure architecture diagram tool known for its simplistic UI and stand-out functionalities. Logging and managing storage resources is effortless, making this tool popular among competitors.

Amazon Web Services 52

Amazon Web Services Data Lake Java SQL 52

Knowledge Hut

JUNE 26, 2023

A complete end-to-end stream processing pipeline is shown here using an architectural diagram. The pipeline in this reference design collects data from two different sources, then conducts a join operation on related records from each stream, then enriches the output, and finally produces an average.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Knowledge Hut

APRIL 25, 2023

Key features: Scalable data storage Fault-tolerant Support for batch processing 9. Apache Airflow Apache Airflow is an open-source platform used for orchestrating complex data pipelines. It provides an extensible architecture that allows data engineers to define, schedule, and monitor workflows.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Knowledge Hut

AUGUST 18, 2023

Foundational The Foundational level is intended for individuals who are new to GCP & cloud computing in general. We can call it the entry level Google cloud certification. This certification covers fundamental concepts such as cloud computing architecture, GCP services, & data storage & processing.

Google Cloud 52

Google Cloud Certification Cloud Cloud Computing 52

Rockset

NOVEMBER 12, 2020

Elasticsearch is one tool to which reads can be offloaded, and, because both MongoDB and Elasticsearch are NoSQL in nature and offer similar document structure and data types, Elasticsearch can be a popular choice for this purpose. This blog post will examine the various tools that can be used to sync data between MongoDB and Elasticsearch.

MongoDB 40

MongoDB NoSQL Data Pipeline Data Storage 40

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. In this post we will provide details of the NMDB system architecture beginning with the system requirements?—?these MDVS also serves as the storehouse and the manager for the data schema itself.

Media 96

Media Database Metadata Data Schemas 96

Confluent

OCTOBER 16, 2019

We want to resolve the location code ( loc_stanox ), and we can do so using the location reference data from the CIF data ingested into a separate Kafka topic and modelled as a KSQL table: SELECT EVENT_TYPE, ACTUAL_TIMESTAMP, LOC_STANOX, S.TPS_DESCRIPTION AS LOCATION_DESCRIPTION FROM TRAIN_MOVEMENTS_00 TM.

Kafka 19

Kafka Building Data Coding 19

AltexSoft

MAY 12, 2023

Tools and platforms for unstructured data management Unstructured data collection Unstructured data collection presents unique challenges due to the information’s sheer volume, variety, and complexity. The process requires extracting data from diverse sources, typically via APIs. Build data architecture.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

ProjectPro

MAY 31, 2021

Data Description: You will use the Covid-19 dataset(COVID-19 Cases.csv) from data.world , for this project, which contains a few of the following attributes: people_positive_cases_count county_name case_type data_source Language Used: Python 3.7 Big Data Analytics Projects for Students on Chicago Crime Data Analysis with Source Code 11.

Big Data 52

Big Data Coding Project Hadoop 52

Cloudera

AUGUST 17, 2022

What if you could access all your data and execute all your analytics in one workflow, quickly with only a small IT team? CDP One is a new service from Cloudera that is the first data lakehouse SaaS offering with cloud compute, cloud storage, machine learning (ML), streaming analytics, and enterprise grade security built-in.

Cloud Computing 114

Cloud Computing Cloud Storage Data Science Government 114

Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? Data-first companies have embraced data platforms as an effective way to aggregate, operationalize, and democratize data at scale across the organization. Snowflake, a cloud data warehouse, is a popular choice among data teams when it comes to quickly scaling up a data platform.

Building 52

Building BI Data Lake Data Governance 52

AltexSoft

MARCH 30, 2023

It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data. The relatively new storage architecture powering Databricks is called a data lakehouse. Databricks lakehouse platform architecture.

Scala 64

Scala Data Lake BI Google Cloud 64

Towards Data Science

NOVEMBER 4, 2023

Indeed, why would we build a data connector from scratch if it already exists and is being managed in the cloud? Very often it is row-based and might become quite expensive on an enterprise level of data ingestion, i.e. big data pipelines. The downside of this approach is it’s pricing model though. Image by author.

Data Engineering 81

Data Engineering Data Engineer Engineering BI 81