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 116

Cloud Computing Cloud Storage Data Science Government 116

Rockset

AUGUST 4, 2021

Although Snowflake is great at querying massive amounts of data, the database still needs to ingest this data. Data ingestion must be performant to handle large amounts of data. Without performant data ingestion, you run the risk of querying outdated values and returning irrelevant analytics.

Data Ingestion 40

Data Ingestion Cloud Storage Data Warehouse Architecture 40

Cloudera

MARCH 31, 2021

One of our customers, Commerzbank, has used the CDP Public Cloud trial to prove that they can combine both Google Cloud and CDP to accelerate their migration to Google Cloud without compromising data security or governance. . Data Preparation (Apache Spark and Apache Hive) .

Google Cloud 94

Google Cloud Cloud Amazon Web Services Cloud Storage 94

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

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.

Machine Learning 107

Machine Learning Python Kafka Java 107

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. Load data For data ingestion Google Cloud Storage is a pragmatic way to solve the task.

Bytes 97

Bytes Google Cloud Cloud Storage Utilities 97

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. The data objects are accessible only through SQL query operations run using Snowflake.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

ProjectPro

FEBRUARY 6, 2023

Unlock the ProjectPro Learning Experience for FREE Pub/Sub Project Ideas For Practice Now that you have a fundamental understanding of Google Cloud Pub/Sub and its use cases, here are a few Pub/Sub project ideas you can practice.

Google Cloud 52

Google Cloud Cloud Cloud Storage Data Ingestion 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. Data connectors are an essential part of such a platform. Of course, how else are we going to get the data? So, do what is best for your application.

Coding 75

Coding Cloud Storage Data Data Ingestion 75

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 98

Data Engineering Data Engineer Engineering BI 98

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

DareData

JANUARY 30, 2023

Our goal is to help data scientists better manage their models deployments or work more effectively with their data engineering counterparts, ensuring their models are deployed and maintained in a robust and reliable way. DigDag: An open-source orchestrator for data engineering workflows.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Ascend.io

FEBRUARY 23, 2024

If your core data systems are still running in a private data center or pushed to VMs in the cloud, you have some work to do. To take advantage of cloud-native services, some of your data must be replicated, copied, or otherwise made available to native cloud storage and databases.

Cloud Storage 52

Cloud Storage Data Ingestion Machine Learning Algorithm 52

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. What are the Different Storage Layers Available in Snowflake? They are flexible, secure, and provide exceptional performance.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

Machine Learning 85

Machine Learning Algorithm Government Metadata 85

ProjectPro

AUGUST 24, 2021

Data Engineering Project for Beginners If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below. This big data project discusses IoT architecture with a sample use case.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Ascend.io

JUNE 6, 2023

Strategies to Reduce Storage Costs The Ascend platform leverages two effective techniques designed to keep cloud storage costs under control and optimize your budget. This allows for data ingestion from sources outside the subnet, and access for authenticated users. cents per gigabyte. cents per gigabyte.

Cloud 52

Cloud Data Pipeline Data Ingestion Cloud Storage 52

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. Hadoop, Apache Spark).

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Rockset

MARCH 21, 2019

This makes turning any type of data—from JSON, XML, Parquet, and CSV to even Excel files—into SQL tables a trivial pursuit. We automatically build multiple general-purpose indexes on all data ingested into Rockset, so that we can eliminate the need for database administration and query tuning for a wide spectrum of applications.

SQL 52

SQL Data Management Management Engineering 52

Monte Carlo

APRIL 24, 2023

We continuously hear data professionals describe the advantage of the Snowflake platform as “it just works.” Snowpipe and other features makes Snowflake’s inclusion in this top data lake vendors list a no-brainer. It’s frustrating…[Lake Formation] is a step-level change for how easy it is to set up data lakes,” he said.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Rockset

MARCH 1, 2023

Developers can spin up or down virtual instances based on the performance requirements of their streaming ingest or query workloads. In addition, Rockset provides fast data access through the use of more performant hot storage, while cloud storage is used for durability.

Architecture 59

Architecture AWS SQL Cloud Storage 59

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

Knowledge Hut

JUNE 26, 2023

Finnhub API with Kafka for Real-Time Financial Market Data Pipeline Project Overview: The goal of this project is to construct a streaming data pipeline by making use of the real-time financial market data API provided by Finnhub.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

WeCloudData

OCTOBER 19, 2021

Conclusion WeCloudData helped a client build a flexible data pipeline to address the needs from multiple business units requiring different sets, views and timelines of job market data.

Consulting 52

Consulting Raw Data Data Lake Data Pipeline 52

WeCloudData

OCTOBER 19, 2021

Conclusion WeCloudData helped a client build a flexible data pipeline to address the needs from multiple business units requiring different sets, views and timelines of job market data.

Consulting 52

Consulting Raw Data Data Lake Data Pipeline 52

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

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

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 Machine Learning BI 64

Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? Recently, there’s been a lot of discussion around whether to go with open source or closed source solutions (the dialogue between Snowflake and Databricks’ marketing teams really brings this to light) when it comes to building your data platform.

Building 52

Building BI Data Lake Data Governance 52

Monte Carlo

OCTOBER 5, 2023

Aligning with stakeholders: SLAs, SLIs, and SLOs Many organizations adopt an approach to setting data quality standards that will be familiar to stakeholders: SLAs (service-level agreements), SLIs (service-level indicators), and SLOs (service-level objectives).

Government 52

Government Data Governance Data Cloud Storage 52

Netflix Tech

DECEMBER 14, 2018

MDVS also serves as the storehouse and the manager for the data schema itself. As was noted in the previous post , data schema could itself evolve over time, but all the data, ingested hitherto, has to remain compliant with the latest schema. NMDB leverages a cloud storage service (e.g.,

Media 97

Media Database Metadata Data Schemas 97

Monte Carlo

FEBRUARY 15, 2023

Key Functions of a Data Warehouse Any data warehouse should be able to load data, transform data, and secure data. Data Loading This is one of the key functions of any data warehouse. Data can be loaded in batches or can be streamed in near real-time. They need to be transformed.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Knowledge Hut

APRIL 25, 2023

Key features of Amazon Redshift: Columnar storage for efficient data storage and retrieval Advanced compression techniques for reducing storage costs Automatic optimization of queries for faster performance Integration with AWS data lake services for easy data ingestion Scalability and elasticity to handle growing data volumes 2.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 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

Knowledge Hut

JULY 6, 2023

However, there are costs associated with data ingestion. Logging and managing storage resources is effortless, making this tool popular among competitors. Cloud Combine is popular among Azure DevTools for teaching because of its simplicity and beginner-friendly UI.

Amazon Web Services 52

Amazon Web Services Data Lake Java SQL 52

ProjectPro

DECEMBER 7, 2021

However, you can also pull data from centralized data sources like data warehouses to transform data further and build ETL pipelines for training and evaluating AI agents. Processing: It is a data pipeline component that decides the data flow implementation.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Knowledge Hut

AUGUST 18, 2023

Google Cloud Associate Cloud Engineer Certification (a) Certification Overview This Google platform certification is for individuals who have hands-on experience with Google Cloud & want to showcase their expertise in cloud technology. in the Google Cloud environment. (b)

Google Cloud 52

Google Cloud Certification Cloud Cloud Computing 52