Edureka

OCTOBER 8, 2024

Database Management: Storing, retrieving data, and managing it effectively are vital. Full Stack Developers are adept at working with databases, whether they are SQL-based like MySQL or No SQL like MongoDB. They store data in tables and have relationships between data. Popular choices are MySQL or PostgreSQL.

MongoDB 40

MongoDB NoSQL MySQL PostgreSQL 40

Striim

JUNE 11, 2024

It is the quintessential solution for safeguarding FinServ transactions as it stands out by seamlessly integrating predictive analytics with real-time data processing. By selectively capturing and replicating changes made to source data, Striim minimizes latency and resource utilization.

MongoDB 52

MongoDB MySQL Utilities Algorithm 52

Edureka

MARCH 5, 2024

Data processing, business logic implementation, and server-side routing are some of the tasks that backend developers handle. Database Management : Designing, implementing, and managing databases to store and retrieve data effectively. The MERN stack comprises MongoDB, Express.js, React.js, and Node.js.

MongoDB 40

MongoDB PostgreSQL MySQL Java 40

ProjectPro

MARCH 19, 2015

Big Data NoSQL databases were pioneered by top internet companies like Amazon, Google, LinkedIn and Facebook to overcome the drawbacks of RDBMS. RDBMS is not always the best solution for all situations as it cannot meet the increasing growth of unstructured data.

NoSQL 49

NoSQL Big Data SQL Database-centric 49

AltexSoft

MAY 12, 2023

A fixed schema means the structure and organization of the data are predetermined and consistent. It is commonly stored in relational database management systems (DBMSs) such as SQL Server, Oracle, and MySQL, and is managed by data analysts and database administrators. Common formats include XML, JSON, and CSV.

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

Knowledge Hut

DECEMBER 26, 2023

In other words, they develop, maintain, and test Big Data solutions. They use technologies like Storm or Spark, HDFS, MapReduce, Query Tools like Pig, Hive, and Impala, and NoSQL Databases like MongoDB, Cassandra, and HBase. To become a Big Data Engineer, knowledge of Algorithms and Distributed Computing is also desirable.

Data Science 97

Data Science BI Machine Learning Business Intelligence 97

Knowledge Hut

APRIL 18, 2023

is particularly popular among companies requiring real-time data processing, such as chat applications, gaming platforms, and streaming services, as it provides a highly scalable, event-driven architecture that can handle many simultaneous connections without blocking. Data Handling Node.js

Architecture 97

Architecture Database Coding Programming 97

Knowledge Hut

MARCH 27, 2024

Big data tools are used to perform predictive modeling, statistical algorithms and even what-if analyses. Some important big data processing platforms are: Microsoft Azure. Why Is Big Data Analytics Important? Some open-source technology for big data analytics are : Hadoop. Apache Spark. Apache Storm. Apache SAMOA.

Big Data 52

Big Data Data Analytics MongoDB Big Data Tools 52

ProjectPro

OCTOBER 28, 2015

Apache Sqoop (SQL-to-Hadoop) is a lifesaver for anyone who is experiencing difficulties in moving data from the data warehouse into the Hadoop environment. Apache Sqoop is an effective hadoop tool used for importing data from RDBMS’s like MySQL, Oracle, etc. into HBase, Hive or HDFS. Sqoop is not event-driven.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

Ascend.io

SEPTEMBER 14, 2023

PySpark, for instance, optimizes distributed data operations across clusters, ensuring faster data processing. Use Case: Transforming monthly sales data to weekly averages import dask.dataframe as dd data = dd.read_csv('large_dataset.csv') mean_values = data.groupby('category').mean().compute()

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

Rockset

NOVEMBER 28, 2022

using Amazon’s Database Migration Service (DMS), we now continuously replicate data from Aurora into Rockset, which does all of the data processing, aggregations and calculations.” We’ve already built CDC-based data connectors for many common sources: DynamoDB , MongoDB , and more.

Data Warehouse 52

Data Warehouse MongoDB PostgreSQL SQL 52

AltexSoft

OCTOBER 21, 2022

popular SQL and NoSQL database management systems including Oracle, SQL Server, Postgres, MySQL, MongoDB, Cassandra, and more; cloud storage services — Amazon S3, Azure Blob, and Google Cloud Storage; message brokers such as ActiveMQ, IBM MQ, and RabbitMQ; Big Data processing systems like Hadoop ; and.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

ProjectPro

JANUARY 31, 2023

Data Storage: The next step after data ingestion is to store it in HDFS or a NoSQL database such as HBase. HBase storage is ideal for random read/write operations, whereas HDFS is designed for sequential processes. Data Processing: This is the final step in deploying a big data model. How to avoid the same.

Big Data 52

Big Data Hadoop Relational Database AWS 52

Rockset

JUNE 7, 2021

This often leads to data being pulled in batches anywhere from large batches pulled once a day to lots of small batches pulled frequently. The rule of thumb is that if you are looking to build a real-time data processing system then the push approach should be used.

IT 40

IT Kafka Database MongoDB 40

ProjectPro

NOVEMBER 14, 2015

Hadoop projects make optimum use of ever-increasing parallel processing capabilities of processors and expanding storage spaces to deliver cost-effective, reliable solutions. Owned by Apache Software Foundation, Apache Spark is an open-source data processing framework. Why Apache Spark?

Hadoop 52

Hadoop Project Big Data Healthcare 52

Databand.ai

DECEMBER 13, 2022

Follow Zach on LinkedIn 8) Shashank Mishra Data Engineer III at Expedia Group Shashank is a data engineer with over six years of experience working in service and product companies, having solved data mysteries across aviation, pharmaceutical, fintech, and telecom companies and designed scalable and optimized data pipelines to handle petabytes of data (..)

Data Engineering 52

Data Engineering Data Engineer Engineering AWS 52

ProjectPro

DECEMBER 7, 2021

Big data pipelines must be able to recognize and process data in various formats, including structured, unstructured, and semi-structured, due to the variety of big data. Over the years, companies primarily depended on batch processing to gain insights. However, it is not straightforward to create data pipelines.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

JULY 23, 2021

Real-time Data Processing Application 7. Real-time Data Processing Application The goal is to process the high-volume data quantities in real-time with no compromises on the accuracy of the outcomes. Then, with Amazon Kinesis, build data analytics streams for real-time data streaming.

AWS 52

AWS Project Amazon Web Services Cloud Computing 52

AltexSoft

OCTOBER 8, 2021

It is possible to move datasets with incremental loading (when only new or updated pieces of information are loaded) and bulk loading (lots of data is loaded into a target source within a short period of time). MongoDB), SQL databases (e.g., MySQL), file stores (e.g., Hadoop), cloud data warehouses (e.g., Data loading.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

Knowledge Hut

SEPTEMBER 6, 2023

Data Analysis : Strong data analysis skills will help you define ways and strategies to transform data and extract useful insights from the data set. Big Data Frameworks : Familiarity with popular Big Data frameworks such as Hadoop, Apache Spark, Apache Flink, or Kafka are the tools used for data processing.

Big Data 52

Big Data Certification Hadoop Kafka 52

ProjectPro

AUGUST 24, 2021

to accumulate data over a given period for better analysis. There are many more aspects to it and one can learn them better if they work on a sample data aggregation project. Project Idea: Explore what is real-time data processing, the architecture of a big data project, and data flow by working on a sample of big data.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

JULY 27, 2021

Data Engineer Interview Questions on Big Data Any organization that relies on data must perform big data engineering to stand out from the crowd. But data collection, storage, and large-scale data processing are only the first steps in the complex process of big data analysis.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

U-Next

AUGUST 3, 2022

Database Management: A Data Scientist has to have a solid understanding of data processing and data managerial staff, in addition to being skilled with machine learning and statistical models. They must organise, integrate, clean, and arrange a sizable amount of data to make it ready for future usage.

Data Science 52

Data Science Computer Science Retail Data Mining 52