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

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 consults with companies in multiple industries, helping them develop their data infrastructure and solutions.

SQL 52

SQL Data Ingestion Consulting Data Pipeline 52

Rockset

JULY 6, 2022

Incoming data that does not match the predefined attributes or data types is automatically rejected by the database, with a null value stored in its place or the entire record skipped completely. Companies carefully engineered their ETL data pipelines to align with their schemas (not vice-versa).

NoSQL 52

NoSQL SQL Systems PostgreSQL 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

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? Why is data pipeline architecture important?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Rockset

DECEMBER 17, 2020

Overview of the Customer 360 App Our app will make use of real-time data on customer orders and events. We’ll use Rockset to get data from different sources and run analytical queries that power our app in Retool. For our example, DynamoDB will store customers’ orders, and we will get the customer_events stream through Amazon Kinesis.

Building 40

Building Aggregated Data SQL Data Ingestion 40

DareData

JANUARY 30, 2023

In this post, we'll discuss some key data engineering concepts that data scientists should be familiar with, in order to be more effective in their roles. These concepts include concepts like data pipelines, data storage and retrieval, data orchestrators or infrastructure-as-code.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

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

Ascend.io

JUNE 8, 2023

The Essential Six Capabilities To set the stage for impactful and trustworthy data products in your organization, you need to invest in six foundational capabilities. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 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

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

ProjectPro

AUGUST 24, 2021

Data Sourcing: Building pipelines to source data from different company data warehouses is fundamental to the responsibilities of a data engineer. So, work on projects that guide you on how to build end-to-end ETL/ELT data pipelines. Google BigQuery receives the structured data from workers.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

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

Monte Carlo

FEBRUARY 15, 2023

Second, to reduce your time-to-detection you need to be end-to-end across your entire data system which may include warehouses or lakes from other vendors or other components of the modern data stack. Finally, where and how the data pipeline broke isn’t always obvious. They need to be transformed.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 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

Rockset

SEPTEMBER 3, 2020

Every change happening to the data stored in MongoDB should eventually be recorded in the oplog. This will be read by the service and applied to the data in PostgreSQL. If this one pipeline fails, data replication to PostgreSQL stops, creating a situation where the data in MongoDB and the data in PostgreSQL are not the same.

PostgreSQL 40

PostgreSQL MongoDB SQL Database 40

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

ProjectPro

DECEMBER 6, 2016

Pentaho published a whitepaper titled “Hadoop and the Analytic Data Pipeline” that highlights the key categories which need to be focused on - Big Data Ingestion, Transformation, Analytics, Solutions. Azure can process structured and structured data with no limits.

Hadoop 52

Hadoop Data Lake BI Big Data 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

ProjectPro

NOVEMBER 15, 2021

To execute pipelines, beam supports numerous distributed processing back-ends, including Apache Flink, Apache Spark , Apache Samza, Hazelcast Jet, Google Cloud Dataflow, etc. With SQL, machine learning, real-time data streaming, graph processing, and other features, this leads to incredibly rapid big data processing.

Big Data 52

Big Data Project Metadata Programming Language 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

AltexSoft

JULY 29, 2022

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. You are welcome to explore them yourself to find the right instruments for your requirements and for data engineering of any complexity.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

ProjectPro

DECEMBER 28, 2021

Apache Spark Streaming Use Cases There are over 3000 companies that use Spark Streaming including companies like Zendesk, Uber, Netflix, and Pinterest To create real-time telemetry analytics, Uber collects terabytes of event data every day from their mobile users. live logs, IoT device data, system telemetry data, etc.)

Architecture 52

Architecture Kafka Java Scala 52

ProjectPro

OCTOBER 20, 2021

Having multiple data integration routes helps optimize the operational as well as analytical use of data. Experimentation in production Big Data Data Warehouse for core ETL tasks Direct data pipelines Tiered Data Lake 4. Data: Data Engineering Pipelines Data is everything.

Machine Learning 52

Machine Learning Algorithm Government Data Science 52

ProjectPro

MAY 31, 2021

Below is a list of Big Data project ideas and an idea of the approach you could take to develop them; hoping that this could help you learn more about Big Data and even kick-start a career in Big Data. Machines and humans are both sources of structured data.

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. See the Snowflake documentation for more info and get started today using the Quickstart Guide.

Medical 57

Medical Retail Python Pharmaceutical 57

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

Data storage The tools mentioned in the previous section are instrumental in moving data to a centralized location for storage, usually, a cloud data warehouse, although data lakes are also a popular option. But this distinction has been blurred with the era of cloud data warehouses.

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.

Data Warehouse 52

Data Warehouse Pipeline-centric Government Data 52