Knowledge Hut

APRIL 23, 2024

Example of Data Variety An instance of data variety within the four Vs of big data is exemplified by customer data in the retail industry. Customer data come in numerous formats. It can be structured data from customer profiles, transaction records, or purchase history.

Big Data 52

Big Data Media Datasets Unstructured Data 52

RandomTrees

SEPTEMBER 6, 2020

A combination of structured and semi structured data can be used for analysis and loaded into the cloud database without the need of transforming into a fixed relational scheme first. The Data Load Accelerator meets the above-mentioned solution. Here’s a detail on the architecture of Snowflake.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Data Engineering Weekly

JUNE 4, 2023

link] KOHO: Handling Schema Evolution in the Data Pipelines at KOHO Schema management at the data ingestion service and the DLQ (Dead Letter Queue) pattern is emerging as the standard architecture pattern in event processing. The author writes an excellent introduction to vector databases and their applications.

Data Engineering 52

Data Engineering Data Engineer Engineering Medical 52

Databand.ai

JULY 17, 2023

Choosing Between DataOps and MLOps Evaluating Your Organization's Needs To choose the right approach for your organization, consider these factors: Type of data processing: If you primarily work with structured or semi-structured data and need a streamlined process for managing pipelines, DataOps might be more suitable.

Data Pipeline 52

Data Pipeline Machine Learning High Quality Data BI 52

Rockset

NOVEMBER 5, 2021

Solution 2: Ingest Dynamic, Semi-Structured Data Rockset supports schemaless ingestion of raw semi-structured data. By adopting Rockset, DataBrain didn’t need to hire a data engineer just to manage ETL scripts.

PostgreSQL 52

PostgreSQL Structured Data Data Lake Data Ingestion 52

Rockset

SEPTEMBER 8, 2021

At Rockset, we work hard to build developer tools (as well as APIs and SDKs) that allow you to easily consume semi-structured data using SQL and run sub-second queries on real-time data.

SQL 52

SQL Data Ingestion Consulting Data Pipeline 52

Knowledge Hut

APRIL 25, 2023

It provides a flexible data model that can handle different types of data, including unstructured and semi-structured data. Key features: Flexible data modeling High scalability Support for real-time analytics 4. Key features: Instant elasticity Support for semi-structured data Built-in data security 5.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Rockset

JULY 29, 2022

This fast, serverless, highly scalable, and cost-effective multi-cloud data warehouse has built-in machine learning, business intelligence, and geospatial analysis capabilities for querying massive amounts of structured and semi-structured data. The Snowpipe feature manages continuous data ingestion.

Data Analytics 52

Data Analytics Data Warehouse Datasets Google Cloud 52

Rockset

JULY 28, 2022

Documents in MongoDB can also have complex structures. Data is stored as JSON documents that can contain nested objects and arrays that all provide further intricacies when building up analytical queries on the data such as accessing nested properties and exploding arrays to analyze individual elements.

MongoDB 52

MongoDB Kafka NoSQL Data Lake 52

Silectis

JANUARY 3, 2021

Data Engineering Data engineering is a process by which data engineers make data useful. Data engineers design, build, and maintain data pipelines that transform data from a raw state to a useful one, ready for analysis or data science modeling. Database A collection of structured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

Rockset

JULY 6, 2022

When it encounters semi-structured data that does not fit neatly into its existing tables and databases, it simply stores the data as a JSON-like blob. This keeps the data intact. It's not true and is just one of many outdated data myths that modern offerings such as Rockset are busting.

NoSQL 52

NoSQL SQL Systems PostgreSQL 52

ProjectPro

DECEMBER 7, 2021

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. Step 1- Automating the Lakehouse's data intake.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 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. AWS is one of the most popular data lake vendors. A picture of their Lake Formation architecture.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Monte Carlo

JULY 10, 2023

Data can go missing for nearly endless reasons, but here are a few of the most common challenges around data completeness: Inadequate data collection processes Data collection and data ingestion can cause data completion issues when collection procedures aren’t standardized, requirements aren’t clearly defined, and fields are incomplete or missing.

Data Collection 40

Data Collection Data Governance Government Data 40

Rockset

DECEMBER 17, 2020

Deploy a SQL Query as an API on Rockset Once we’ve connected our data sources and created data collections in Rockset, we can start writing queries. On Rockset, we can use SQL queries to extract meaningful insights from raw semi-structured data ingested without a predefined schema.

Building 40

Building Aggregated Data SQL Data Ingestion 40

ProjectPro

JULY 22, 2016

Data warehouses do a good job for what they are meant to do, but with disparate data sources and different data types like transaction logs, social media data, tweets, user reviews, and clickstream data –Data Lakes fulfil a critical need. Data Warehouses do not retain all data whereas Data Lakes do.

Data Lake 40

Data Lake Data Warehouse Hadoop Unstructured Data 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

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

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Key differences between structured, semi-structured, and unstructured data.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Ascend.io

JUNE 8, 2023

Acting as the core infrastructure, data pipelines include the crucial steps of data ingestion, transformation, and sharing. Data Ingestion Data in today’s businesses come from an array of sources, including various clouds, APIs, warehouses, and applications.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

The Modern Data Company

MAY 8, 2023

It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs. Data is stored in a schema-on-write approach, which means data is cleaned, transformed, and structured before storing.

Data Management 52

Data Management Management Data Lake Data Governance 52

The Modern Data Company

MAY 8, 2023

It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs. Data is stored in a schema-on-write approach, which means data is cleaned, transformed, and structured before storing.

Data Management 52

Data Management Data Lake Management Data Governance 52

The Modern Data Company

MAY 8, 2023

It can store any type of data — structured, unstructured, and semi-structured — in its native format, providing a highly scalable and adaptable solution for diverse data needs. Data is stored in a schema-on-write approach, which means data is cleaned, transformed, and structured before storing.

Data Management 52

Data Management Management Data Lake Data Governance 52

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? 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.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

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

Data Engineering Weekly

NOVEMBER 20, 2022

With Upsolver SQLake, you build a pipeline for data in motion simply by writing a SQL query defining your transformation. I'm a little more curious to understand the design in detail to see the data catalog as an integral part of the pipeline design.

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

ProjectPro

OCTOBER 28, 2015

Getting data into the Hadoop cluster plays a critical role in any big data deployment. Data ingestion is important in any big data project because the volume of data is generally in petabytes or exabytes. Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc.,

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

Monte Carlo

FEBRUARY 15, 2023

Yes, data warehouses can store unstructured data as a blob datatype. Data Transformation Raw data ingested into a data warehouse may not be suitable for analysis. Data engineers use SQL, or tools like dbt, to transform data within the data warehouse. They need to be transformed.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Snowflake

MARCH 30, 2023

3EJHjvm Once a business need is defined and a minimal viable product ( MVP ) is scoped, the data management phase begins with: Data ingestion: Data is acquired, cleansed, and curated before it is transformed. Feature engineering: Data is transformed to support ML model training. ML workflow, ubr.to/3EJHjvm

Engineering 82

Engineering Raw Data Data Science Scala 82

Towards Data Science

MARCH 5, 2024

The storage system is using Capacitor, a proprietary columnar storage format by Google for semi-structured data and the file system underneath is Colossus, the distributed file system by Google. Load data For data ingestion Google Cloud Storage is a pragmatic way to solve the task. Also this query comes at 0 costs.

Bytes 75

Bytes Google Cloud Cloud Storage Utilities 75

Rockset

MARCH 27, 2019

You have complex, semi-structured data—nested JSON or XML, for instance, containing mixed types, sparse fields, and null values. It's messy, you don't understand how it's structured, and new fields appear every so often. This enables Rockset to generate a Smart Schema on the data.

Raw Data 40

Raw Data SQL NoSQL Datasets 40

ProjectPro

JANUARY 25, 2022

Easy Processing- PySpark enables us to process data rapidly, around 100 times quicker in memory and ten times faster on storage. When it comes to data ingestion pipelines, PySpark has a lot of advantages. PySpark allows you to process data from Hadoop HDFS , AWS S3, and various other file systems.

Big Data 52

Big Data Data Process Process Kafka 52

ProjectPro

JANUARY 31, 2023

There are three steps involved in the deployment of a big data model: Data Ingestion: This is the first step in deploying a big data model - Data ingestion, i.e., extracting data from multiple data sources. Data Variety Hadoop stores structured, semi-structured and unstructured data.

Big Data 52

Big Data Hadoop AWS Relational Database 52

Cloudera

JANUARY 22, 2019

Today’s data landscape is characterized by exponentially increasing volumes of data, comprising a variety of structured, unstructured, and semi-structured data types originating from an expanding number of disparate data sources located on-premises, in the cloud, and at the edge. Data orchestration.

Big Data 44

Big Data NoSQL Data Lake Hadoop 44

Rockset

OCTOBER 4, 2022

Data Ingestion Streaming vs Batch Ingestion While ClickHouse offers several ways to integrate with Kafka to ingest event streams, including a native connector, ClickHouse ingests data in batches. Data Model In most cases, ClickHouse will require users to specify a schema for any table they create.

MySQL 52

MySQL Kafka Aggregated Data Architecture 52

Knowledge Hut

JANUARY 18, 2024

Let us now look into the differences between AI and Data Science: Data Science vs Artificial Intelligence [Comparison Table] SI Parameters Data Science Artificial Intelligence 1 Basics Involves processes such as data ingestion, analysis, visualization, and communication of insights derived.

Data Science 98

Data Science Deep Learning Business Analyst Data Mining 98

Cloudera

SEPTEMBER 6, 2018

The solution combines Cloudera Enterprise , the scalable distributed platform for big data, machine learning, and analytics, with riskCanvas , the financial crime software suite from Booz Allen Hamilton. It supports a variety of storage engines that can handle raw files, structured data (tables), and unstructured data.

Banking 40

Banking Machine Learning Big Data Scala 40