As per a recent report by Forbes, the world has witnessed a 5000% increase in data interactions over the last decade. From 2010 to 2020, the total amount of data has soared from 1.2 trillion gigabytes to a staggering 60 trillion gigabytes. To put this into perspective, this number is 2,000,000 times the entire catalog of Netflix.
But, how on earth did we manage to handle this colossal amount of data? The answer lies in data engineering, aka, information engineering.
Simply put, data engineering is a process of creating systems to organize, unify, and manage huge amounts of data collected from multiple sources, making it easily accessible for anyone who needs it. The power of data can be unlocked for everyone with the right tools. With their high-end capabilities and user-friendly interfaces, these tools are making it easier than ever to extract insights and make informed decisions.
Top Data Engineering tools –
1. Apache Spark
2. Snowflake
3. Amazon Redshift
4. Tableau
5. Apache Kafka
6. Google BigQuery
7. Microsoft Power BI
Apache Spark
The game-changing open-source big data processing framework is known for its lightning-fast speed. Apache Spark had over 365,000 meetup members in 2017, and it’s one of the most data engineering tools in the Hadoop ecosystem. But what really sets Apache Spark apart is its wide range of offerings. It provides high-level APIs in Java, Scala, Python, and R, along with various machine learning and graph analysis libraries such as MLib, GraphX, Spark SQL, and Spark Stream.
Apache Spark is replete with impressive features for streaming code. From stream mining to real-time scoring of analytic models, companies like FINRA, Yelp, Zillow, and DataXu are all making the most of what Apache Spark has to offer.
Snowflake
Snowflake is a highly scalable SaaS solution built on top of Amazon Web Services, Google Cloud, and Microsoft Azure. It’s compatible with any cloud vendor and lets you store, process, and analyze data in quick time from any location. And the best part? Multiple data workloads can run independently, making it perfect for data warehousing, data lakes, data science, data sharing, and data engineering.
Snowflake is a more “serverless” option. In other words, no more managing or installing any software or hardware. You just need to provide the number and size of computing clusters, depending on your . With offerings like Snowpipes for data ingestion, interactive reporting for business intelligence, Snowflake Marketplace for data sharing and collaboration, and features that support machine learning, Snowflake is a popular choice among data engineers.
Amazon Redshift
The cloud-based data warehouse service stores vast amounts of data is known for its cost-effectiveness, speed, and ability to provide insights from structured, unstructured, or semi-structured data. Amazon Redshift has detailed information dashboards that offer business intelligence and the ability to handle large volumes of data without slowing down. Not to mention, it integrates with other AWS services effortlessly, and provide tips and suggestions to improve data queries while automating repetitive tasks. Amazon Redshift can generate weekly or monthly reports as and when needed and is a go-to-choice for real-time analytics.
Tableau
Tableau is a popular data visualization tool that lets end users to generate beautiful reports and dashboards and converts them into actionable insights. It is good for hosting dashboards and data sources. You can configure users, groups, and permissions through simple processes, customize notifications, and specify a time interval for snapshots. Besides, Tableau has good data scalability and a secure way of connecting with the data source. It also provides easy connectivity to Excel, CSV files, or any database, and you can filter data in multiple ways to find the most impactful areas.
Apache Kafka
Apacha Kafke is a software system used by over 80% of Fortune companies, including Uber, Square, Strave, Shopify, and Spotify. Kafka is highly reliable, robust, and fault-tolerant, making it the best fit for large-scale applications that require real-time processing. It excels in message processing, website activity tracking, metrics collection and monitoring, logging, event sourcing, and real-time analytics. However, it is not ideal for tasks that don’t require real-time processing, such as data storage or transformation. With Kafka, you can easily track application activity and perform real-time data processing, giving you the edge you need to stay ahead of the competition.
Google BigQuery
Google BigQuery is a data warehouse designed to make working with large sets of data a shoo-in. With BigQuery, data analysts can explore and discover meaningful insights without worrying about the underlying technical infrastructure. It’s serverless and scalable, which means it’s easy to organize and analyze data from different sources. Plus, you can even use it for real-time data capture and analysis. You can also share your insights with others in your organization or embed them in your app or website.
Microsoft Power BI
Microsoft Power BI is a popular business analytics solution to visualize and share insights across an organization or embed them in an app or website. It leverages cutting-edge technologies such as Borg, colossus, Jupiter, and Dremel to ensure optimum performance. Power BI is well-known for its user-friendly interface, collaboration features, and extensive coverage of connectors for data sources like Excel, SQL Server, Oracle, SAP Hana, and more. Power BI is often considered the best option due to its easy-to-use features and helpful Microsoft support.
To read more such articles, visit our homepage
