-
-
scala
Scala 2 compiler and standard library. Bugs at https://github.com/scala/bug; Scala 3 at https://github.com/scala/scala3
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
-
PostgreSQL
Mirror of the official PostgreSQL GIT repository. Note that this is just a *mirror* - we don't work with pull requests on github. To contribute, please see https://wiki.postgresql.org/wiki/Submitting_a_Patch
-
MySQL
MySQL Server, the world's most popular open source database, and MySQL Cluster, a real-time, open source transactional database.
-
AmazonMe
Introducing the AmazonMe webscraper - a powerful tool for extracting data from Amazon.com using the Requests and Beautifulsoup library in Python. This scraper allows users to easily navigate and extract information from Amazon's website.
-
SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
-
-
-
Whereas, Real-time processing involves persistently storing data as it comes in through events in real-time. For example, Companies like Uber and In-Drive use GPS trackers in their fleets of vehicles. Every vehicle’s location, speed, and other data are constantly being sent to a centralized server by the GPS units installed in them. So, the real-time processing system set up by these companies analyzes the data from the GPS units in near real-time. This information is used to give passengers up-to-date updates on things like vehicle locations and expected arrival times.
In addition to Structured Query Language(SQL), we can also use a variety of different programming languages, such as Python, Java, JavaScript, R, Julia, Scala, or any other programming language as long as it supports a basic database connection and functions to perform all of those operations, to connect to databases and perform more advanced query operations on the data. This gives us greater flexibility and allows us to apply custom-created logic to the data.
In addition to Structured Query Language(SQL), we can also use a variety of different programming languages, such as Python, Java, JavaScript, R, Julia, Scala, or any other programming language as long as it supports a basic database connection and functions to perform all of those operations, to connect to databases and perform more advanced query operations on the data. This gives us greater flexibility and allows us to apply custom-created logic to the data.
To analyze the data that is stored in an OLTP system, such as a Postgres or MySQL database, we need to transfer it to an OLAP system or a Data Warehouse like Snowflake.
To analyze the data that is stored in an OLTP system, such as a Postgres or MySQL database, we need to transfer it to an OLAP system or a Data Warehouse like Snowflake.
This particular innovation was primarily driven by the FAANG (now MAANGO) companies ( Facebook (Meta), Amazon, Apple, Netflix, Google, and Oracle ), who have adopted data-driven business models and built advanced data infrastructure to support them. These companies have put a lot of money and time into hiring and developing data engineering talent and technologies. They have also helped create new tools and ways to manage and analyze data at a large scale.
For real-time streaming, we have other frameworks and tools like Apache Kafka, ActiveMQ, and AWS Kinesis.
In addition to Structured Query Language(SQL), we can also use a variety of different programming languages, such as Python, Java, JavaScript, R, Julia, Scala, or any other programming language as long as it supports a basic database connection and functions to perform all of those operations, to connect to databases and perform more advanced query operations on the data. This gives us greater flexibility and allows us to apply custom-created logic to the data.
There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS.
Many businesses and companies are moving and transitioning their entire operations to the cloud to escape headaches associated with hardware breakdowns and regular software updates (as we mentioned earlier). Because of this, companies only have to pay for the resources that they really use, and they can scale their servers to meet any demand. Cloud service providers also provide several different kinds of services to manage large amounts of data and ease the process of storing and processing data, making the entire process much more manageable. According to a Gartner cloud computing infrastructure ranking, the top three cloud platform providers are Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.