phData: Data Engineering

SEPTEMBER 19, 2023

Before diving into the data models for data lakes, let’s look at the difference between a data warehouse and a data lake. There are tools designed specifically to analyze your data lake files, determine the schema, and allow for SQL statements to be run directly off this data.

Data Lake 52

Data Lake Process Metadata Data Warehouse 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

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. He's always sharing helpful tidbits about data engineering !

SQL 52

SQL Data Ingestion Consulting Data Pipeline 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. ETL is the acronym for Extract, Transform, and Load.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 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

DareData

JANUARY 30, 2023

Learn the most important data engineering concepts that data scientists should be aware of. As the field of data science and machine learning continues to evolve, it is increasingly evident that data engineering cannot be separated from it. DigDag: An open-source orchestrator for data engineering workflows.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Monte Carlo

APRIL 24, 2023

A notebook-based environment allows data engineers, data scientists, and analysts to work together seamlessly, streamlining data processing, model development, and deployment. Databricks also pioneered the modern data lakehouse architecture, which combines the best of data lakes and data warehouses.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

ProjectPro

AUGUST 24, 2021

Nevertheless, that is not the only job in the data world. Data professionals who work with raw data like data engineers, data analysts, machine learning scientists , and machine learning engineers also play a crucial role in any data science project.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Data Engineering Weekly

NOVEMBER 20, 2022

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. Sign up free to test out the tool today.

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

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

Knowledge Hut

SEPTEMBER 26, 2023

Effective Data Storage: Azure Synapse offers robust data storage solutions that cater to the needs of modern data-driven organizations. It provides the infrastructure necessary for efficient data storage and management, enabling you to store and access large volumes of data reliably. Key Features of Databricks 1.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 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. For this task, you need a dedicated specialist — a data engineer or ETL developer.

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

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? For example, data may be extracted from multiple sources, reorganized, and joined at different times before ultimately being served to its final destination. Why is data pipeline architecture important? They also recently acquired Apache Flink , another streaming solution.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Snowflake

MARCH 30, 2023

A 2016 data science report from data enrichment platform CrowdFlower found that data scientists spend around 80% of their time in data preparation (collecting, cleaning, and organizing of data) before they can even begin to build machine learning (ML) models to deliver business value. ML workflow, ubr.to/3EJHjvm

Engineering 83

Engineering Raw Data Data Science Scala 83

Monte Carlo

FEBRUARY 15, 2023

Generally, raw data can be unclean and enter into the data warehouse, but once inside data engineers can run data quality checks using DISTINCT , ROW_NUMBER() , etc. Yes, data warehouses can store unstructured data as a blob datatype. Data Security Data Warehouses achieve security in multiple ways.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 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 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 Data Lake Management Data Governance 52

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. The compute engine is based on Dremel and it uses Borg for cluster management, running thousands of Dremel jobs across cluster(s).

Bytes 67

Bytes Google Cloud Cloud Storage Utilities 67

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

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

AltexSoft

JULY 29, 2022

What happens, when a data scientist, BI developer , or data engineer feeds a huge file to Hadoop? Under the hood, the framework divides a chunk of Big Data into smaller, digestible parts and allocates them across multiple commodity machines to be processed in parallel. How data engineering works under the hood.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

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

AltexSoft

MAY 14, 2021

A single car connected to the Internet with a telematics device plugged in generates and transmits 25 gigabytes of data hourly at a near-constant velocity. And most of this data has to be handled in real-time or near real-time. Variety is the vector showing the diversity of Big Data. Big Data analytics processes and tools.

Big Data 78

Big Data Data Analytics IT NoSQL 78

Rockset

SEPTEMBER 3, 2020

But, unlike MongoDB, it uses a SQL-like query language to query even the JSON documents, allowing seasoned data engineers to write ad hoc queries when required. Unlike MongoDB, PostgreSQL also allows you to store data in a more traditional row and column arrangement. Like MongoDB, it provides support for JSON documents.

MongoDB 40

MongoDB PostgreSQL SQL Database 40

ProjectPro

NOVEMBER 22, 2021

According to the Businesswire report , the worldwide big data as a service market is estimated to grow at a CAGR of 36.9% This clearly indicates that the need for Big Data Engineers and Specialists would surge in the future years. PySpark Interview Questions for Data Engineer Q1. from 2019 to 2026, reaching $61.42

Hadoop 52

Hadoop Python Datasets Metadata 52

ProjectPro

DECEMBER 28, 2021

Structured Streaming Spark Streaming Structured Streaming What is Kafka Streaming? Spark Streaming was launched in 2013 to enable data engineers and data scientists to process real-time data from SQL databases, Flume, Amazon Kinesis, etc. live logs, IoT device data, system telemetry data, etc.)

Architecture 52

Architecture Kafka Java Scala 52

ProjectPro

OCTOBER 20, 2021

If Data Science were a linear activity, it would be called Data Engineering. Data Science Teams work in an extremely non-linear & complicated fashion but fail to educate the business about how Science is actually happening within the team. Data: Data Engineering Pipelines Data is everything.

Machine Learning 52

Machine Learning Algorithm Government Data Science 52

ProjectPro

AUGUST 21, 2015

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data. are all examples of unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40

ProjectPro

MAY 31, 2021

A big data project is a data analysis project that uses machine learning algorithms and different data analytics techniques on a large dataset for several purposes, including predictive modeling and other advanced analytics applications. Once you have the data, it's time to start using it.

Big Data 52

Big Data Coding Project Hadoop 52

Snowflake

APRIL 20, 2023

Data Pipelines Snowpipe Streaming – public preview While data generated in real time is valuable, it is more valuable when paired with historical data that helps provide context. Write simple Snowpark programs and deploy them as procedures — great for data engineers building pipelines.

Medical 56

Medical Retail Python Pharmaceutical 56

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. 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.

Scala 64

Scala Data Lake BI Google Cloud 64

AltexSoft

MARCH 14, 2023

That’s why some MDS tools are commercial distributions designed to be low-code or even no-code, making them accessible to data practitioners with minimal technical expertise. This means that companies don’t necessarily need a large data engineering team. Data democratization. Data storage component in a modern data stack.

IT 59

IT Data Warehouse Data Governance Data Lake 59

phData: Data Engineering

JULY 18, 2023

Demands on the cloud data warehouse are also evolving to require it to become more of an all-in-one platform for an organization’s analytics needs. Enter Snowflake The Snowflake Data Cloud is one of the most popular and powerful CDW providers. Testing: Data engineering should be treated as a form of software engineering.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52