Striim

OCTOBER 11, 2024

Capable of Processing High Volumes of Data in Various Formats It’s predicted that the world will generate 181 zettabytes of data by 2025. Since unstructured and semi-structured data account for 80% of the data collected by companies, modern data pipelines need to be capable of efficiently processing these diverse data types.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

Ascend.io

NOVEMBER 21, 2023

Extract The initial stage of the ELT process is the extraction of data from various source systems. This phase involves collecting raw data from the sources, which can range from structured data in SQL or NoSQL servers, CRM and ERP systems, to unstructured data from text files, emails, and web pages.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

Striim

AUGUST 22, 2024

To achieve this, combine data from the sum of your sources. For this purpose, you can use ETL (extract, transform, and load) tools or build a custom data pipeline of your own and send the aggregated data to a target system, such as a data warehouse.

Data Warehouse 52

Data Warehouse Retail Machine Learning Database 52

AltexSoft

AUGUST 4, 2021

A data warehouse (DW) is a data repository that allows for storing and managing all the historical enterprise data, coming from disparate internal and external sources like CRMs, ERPs, flat files, etc. Initially, DWs dealt with structured data presented in tabular forms.

Data Lake 64

Data Lake Data Warehouse ETL Tools Database 64

Rockset

DECEMBER 17, 2020

Essentially, Rockset is an indexing layer on top of DynamoDB and Amazon Kinesis, where we can join, search, and aggregate data from these sources. From there, we’ll create a data API for the SQL query we write in Rockset. When an associate converses with the customer, they can handle the customer’s situation appropriately.

Building 40

Building Aggregated Data SQL Database 40

ProjectPro

JANUARY 25, 2022

PySpark SQL and Dataframes A dataframe is a shared collection of organized or semi-structured data in PySpark. This collection of data is kept in Dataframe in rows with named columns, similar to relational database tables. With PySparkSQL, we can also use SQL queries to perform data extraction.

Big Data 52

Big Data Data Process Process Kafka 52

Rockset

FEBRUARY 25, 2021

For example, you might have to develop a real-time data pipeline using a tool like Kafka just to get the data in a format that allows you to aggregate or join data in a performant manner. Analyze Semi-Structured Data As Is The data feeding modern applications is rarely in neat little tables.

SQL 40

SQL Data Pipeline Kafka Database 40

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

ProjectPro

OCTOBER 28, 2015

Hadoop Sqoop and Hadoop Flume are the two tools in Hadoop which is used to gather data from different sources and load them into HDFS. Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc., However, it is not very suitable for queries requiring low latency or interactive queries.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

Rockset

DECEMBER 22, 2020

By using Rockset, we may have to Tokenize our search fields on ingestion however we make up for it in firstly, the simplicity of processing this data on ingestion as well as easier querying, joining, and aggregating data.

SQL 40

SQL Data MongoDB Building 40

ProjectPro

NOVEMBER 15, 2021

With SQL, machine learning, real-time data streaming, graph processing, and other features, this leads to incredibly rapid big data processing. DataFrames are used by Spark SQL to accommodate structured and semi-structured data. The bedrock of Apache Spark is Spark Core, which is built on RDD abstraction.

Big Data 52

Big Data Project Metadata Programming Language 52

ProjectPro

SEPTEMBER 1, 2015

Coding Approach Using Hadoop MapReduce MapReduce is a powerful programming model for parallelism based on rigid procedural structure. Hadoop MapReduce allows programmers to filter and aggregate data from HDFS to gain meaningful insights from big data.

Hadoop 40

Hadoop Java Unstructured Data SQL 40

ProjectPro

JULY 27, 2021

Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language). SQL works on data arranged in a predefined schema. Non-relational databases support dynamic schema for unstructured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

ProjectPro

AUGUST 24, 2021

Google BigQuery receives the structured data from workers. Finally, the data is passed to Google Data studio for visualization. to accumulate data over a given period for better analysis. The Yelp dataset JSON stream is published to the PubSub topic.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

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