Rockset

APRIL 18, 2023

Under the hood, Rockset utilizes its Converged Index technology, which is optimized for metadata filtering, vector search and keyword search, supporting sub-second search, aggregations and joins at scale. Feature Generation: Transform and aggregate data during the ingest process to generate complex features and reduce data storage volumes.

Unstructured Data 105

Unstructured Data Metadata Machine Learning SQL 105

DareData

JULY 5, 2023

The architecture of a data lake project may contain multiple components, including the Data Lake itself, one or multiple Data Warehouses or one or multiple Data Marts. The Data Lake acts as the central repository for aggregating data from diverse sources in its raw format.

Data Lake 52

Data Lake Building Raw Data ETL Tools 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. The use case is fraud detection for credit card payments.

Machine Learning 107

Machine Learning Python Kafka Java 107

ProjectPro

FEBRUARY 8, 2023

Scale Existing Python Code with Ray Python is popular among data scientists and developers because it is user-friendly and offers extensive built-in data processing libraries. For analyzing huge datasets, they want to employ familiar Python primitive types. Then Redshift can be used as a data warehousing tool for this.

AWS 98

AWS Scala Metadata Data Lake 98

Rockset

NOVEMBER 1, 2022

Here’s an example: SELECT NGRAMS(my_text_string, 1, 3) AS my_text_array, * FROM _input Aggregation It is common to pre-aggregate data before it arrives into Elasticsearch for use cases involving metrics. We often see ingest queries aggregate data by time.

Database-centric 52

Database-centric SQL Pipeline-centric Aggregated Data 52

Ascend.io

MARCH 14, 2023

There are different ways you can make data domain products discoverable and sharable. A spreadsheet might be enough for smaller domains, while more complex domains will likely publish their metadata, owners, origins, sample datasets, and schema to a central repository or catalog. appeared first on Ascend.io.

Government 52

Government Architecture Data Lake Data 52

Ascend.io

MARCH 14, 2023

There are different ways you can make data domain products discoverable and sharable. A spreadsheet might be enough for smaller domains, while more complex domains will likely publish their metadata, owners, origins, sample datasets, and schema to a central repository or catalog. appeared first on Ascend.io.

Government 52

Government Architecture Data Lake Data 52

Rockset

OCTOBER 26, 2022

One was to create another data pipeline that would aggregate data as it was ingested into DynamoDB. And that’s true for small datasets and larger ones. And with the NFL season set to start in less than a month, we were in a bind. A Faster, Friendlier Solution We considered a few alternatives.

SQL 52

SQL NoSQL Database Aggregated Data 52

ProjectPro

JANUARY 25, 2022

Furthermore, PySpark allows you to interact with Resilient Distributed Datasets (RDDs) in Apache Spark and Python. Because of its interoperability, it is the best framework for processing large datasets. Easy Processing- PySpark enables us to process data rapidly, around 100 times quicker in memory and ten times faster on storage.

Big Data 52

Big Data Data Process Process Kafka 52

ProjectPro

DECEMBER 7, 2021

A pipeline may include filtering, normalizing, and data consolidation to provide desired data. It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications. Data ingestion methods gather and bring data into a data processing system.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

NOVEMBER 15, 2021

Multi-node, multi-GPU deployments are also supported by RAPIDS, allowing for substantially faster processing and training on much bigger datasets. TDengine Source: www.taosdata.com TDengine is an open-source big data platform tailored for IoT , linked automobiles, and industrial IoT. Trino Source: trino.io

Big Data 52

Big Data Project Metadata Programming Language 52

AltexSoft

SEPTEMBER 21, 2023

Whether you’re an enterprise striving to manage large datasets or a small business looking to make sense of your data, knowing the strengths and weaknesses of Elasticsearch can be invaluable. With native integrations for major cloud platforms like AWS, Azure, and Google Cloud, sending data to Elastic Cloud is straightforward.

Engineering 52

Engineering NoSQL Programming Language Java 52

phData: Data Engineering

JANUARY 3, 2022

This likely requires you to aggregate data from your ERP system, your supply chain system, potentially third-party vendors, and data around your internal business structure. What if your data is unstructured, and can’t be easily joined together with your other datasets?

Data Engineer 52

Data Engineer Data Engineering Engineering Data Governance 52

AltexSoft

MARCH 14, 2023

Data transformation component in a modern data stack. Cleaning: removing or correcting inaccurate, incomplete, or irrelevant data in the dataset. Normalizing: organizing the data in a standard format to eliminate redundancy and ensure consistency. Transformations may include the following aspects.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Rockset

APRIL 15, 2022

Companies also began to embrace change data capture (CDC) in order to stream updates from operational databases — think Oracle , MongoDB or Amazon DynamoDB — into their data warehouses. Companies also started appending additional related time-stamped data to existing datasets, a process called data enrichment.

Analytics Application 52

Analytics Application Data Warehouse Kafka Raw Data 52