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 Without performant data ingestion, you run the risk of querying outdated values and returning irrelevant analytics.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

phData: Data Engineering

NOVEMBER 8, 2024

This is particularly beneficial in complex analytical queries, where processing smaller, targeted segments of data results in quicker and more efficient query execution. Additionally, the optimized query execution and data pruning features reduce the compute cost associated with querying large datasets.

Architecture 52

Architecture Systems Data Lake Google Cloud 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. Stanford's Relational Databases and SQL.

Data Engineer 52

Data Engineer Data Engineering NoSQL Engineering 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?

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

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 69

Bytes Google Cloud Cloud Storage Utilities 69

Knowledge Hut

SEPTEMBER 26, 2023

Supports Structured and Unstructured Data: One of Azure Synapse's standout features is its versatility in handling a wide array of data types. Whether your data is structured, like traditional relational databases, or unstructured, such as textual data, images, or log files, Azure Synapse can manage it effectively.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

AltexSoft

JULY 29, 2022

On top of HDFS, the Hadoop ecosystem provides HBase , a NoSQL database designed to host large tables, with billions of rows and millions of columns. To facilitate data ingestion, there are Apache Flume aggregating log data from multiple servers and Apache Sqoop designed to transport information between Hadoop and relational (SQL) databases.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59