1597748640
Building a notebook-based ETL framework with Spark and Delta Lake
The process of extracting, transforming and loading data from disparate sources (ETL) have become critical in the last few years with the growth of data science applications. In addition, data availability, timeliness, accuracy and consistency are key requirements at the beginning of any data project.
Even though there are guidelines, there is not a one-fits-all architecture to build ETL data pipelines. It depends on multiple factors such as the type of the data, the frequency, the volume and the expertise of the people that will be maintaining these. Data pipelines need to be reliable and scalable but also relatively straight forward for data engineers and data scientists to integrate with new sources and make changes to the underlying data structures.
There is a myriad of tools that can be used for ETL but Spark is probably one of the most used data processing platforms due to it speed at handling large data volumes. In addition to data processing, Spark has libraries for machine learning, streaming, data analytics among others so it’s a great platform for implementing end-to-end data projects. It also supports Python (PySpark) and R (SparkR, sparklyr), which are the most used programming languages for data science.
#data-science #data-engineering #etl #delta-lake #spark
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1597748640
Building a notebook-based ETL framework with Spark and Delta Lake
The process of extracting, transforming and loading data from disparate sources (ETL) have become critical in the last few years with the growth of data science applications. In addition, data availability, timeliness, accuracy and consistency are key requirements at the beginning of any data project.
Even though there are guidelines, there is not a one-fits-all architecture to build ETL data pipelines. It depends on multiple factors such as the type of the data, the frequency, the volume and the expertise of the people that will be maintaining these. Data pipelines need to be reliable and scalable but also relatively straight forward for data engineers and data scientists to integrate with new sources and make changes to the underlying data structures.
There is a myriad of tools that can be used for ETL but Spark is probably one of the most used data processing platforms due to it speed at handling large data volumes. In addition to data processing, Spark has libraries for machine learning, streaming, data analytics among others so it’s a great platform for implementing end-to-end data projects. It also supports Python (PySpark) and R (SparkR, sparklyr), which are the most used programming languages for data science.
#data-science #data-engineering #etl #delta-lake #spark
1597139280
Decoding The Delta Lake Architecture: What Is It ?
What is Delta Lake?
Delta Lake is an open-source storage layer that delivers reliability to data lakes. Delta Lake implements ACID transactions, scalable metadata handling, and unifies the streaming and batch data processing. Delta Lake architecture runs on top of your current data lake and is fully cooperative with Apache Spark APIs.
Why Delta Lake?
Are we making progress? Well, let’s see what are the main benefits of implementing a Delta Lake in your company.
The Predicament with current Data Architectures
Current big data architectures are challenging to develop, manage, and maintain. Most contemporary data architectures use a mix of at least three varying types of systems: streaming systems, data lakes, and data warehouses. Business data comes through streaming networks such as Amazon Kinesis or Apache Kafka, which mainly focus on accelerated delivery. Then, data is collected in data lakes, such as Apache Hadoop or Amazon S3, which are optimized for large-scale, ultra-low-cost storage. Lamentably, data lakes individually do not have the performance and quality required to support high-end business applications: thus, the most critical data is uploaded to data warehouses, which are optimized for significant performance, concurrency, and security at a much higher storage cost than data lakes.
Delta Lake architecture, Lambda Architecture
Lambda architecture is a traditional technique where a batch system and streaming system prepare records in correspondence. The results are then merged during query time to provide an entire answer. Strict latency requirements to process old and newly formed events made this architecture famous. The key downside to this architecture is the development and operational overhead of maintaining two different systems. There have been efforts to ally batch and streaming into a single system in history. Companies have not been that victorious though in those attempts. With the arrival of Delta Lake, we are seeing a lot of our clients adopting a simple constant data flow model to process data as it comes. We call this architecture, The Delta Lake architecture. We cover the essential bottlenecks for using a continuous data flow model and how the Delta architecture resolves those difficulties.
#big data engineering #blogs #delta lake #delta lake architecture #delta lake spark
1606890999
Top Spark Development Companies | Best Spark Developers - TopDevelopers.co
An extensively researched list of top Apache spark developers with ratings & reviews to help find the best spark development Companies around the world.
Our thorough research on the ace qualities of the best Big Data Spark consulting and development service providers bring this list of companies. To predict and analyze businesses and in the scenarios where prompt and fast data processing is required, Spark application will greatly be effective for various industry-specific management needs. The companies listed here have been skillfully boosting businesses through effective Spark consulting and customized Big Data solutions.
Check out this list of Best Spark Development Companies with Best Spark Developers.
#spark development service providers #top spark development companies #best big data spark development #spark consulting #spark developers #spark application
1595344320
Wondering how to upgrade your skills in the pandemic? Here's a simple way you can do it.
Corona Virus Pandemic has brought the world to a standstill.
Countries are on a major lockdown. Schools, colleges, theatres, gym, clubs, and all other public places are shut down, the country’s economy is suffering, human health is on stake, people are losing their jobs and nobody knows how worse it can get.
Since most of the places are on lockdown, and you are working from home or have enough time to nourish your skills, then you should use this time wisely! We always complain that we want some ‘time’ to learn and upgrade our knowledge but don’t get it due to our ‘busy schedules’. So, now is the time to make a ‘list of skills’ and learn and upgrade your skills at home!
And for the technology-loving people like us, Knoldus Techhub has already helped us a lot in doing it in a short span of time!
If you are still not aware of it, don’t worry as Georgia Byng has well said,
“No time is better than the present”
– Georgia Byng, a British children’s writer, illustrator, actress and film producer.
No matter if you are a developer (be it front-end or back-end) or a data scientist, tester, or a DevOps person, or, a learner who has a keen interest in technology, Knoldus Techhub has brought it all for you under one common roof.
From technologies like Scala, spark, elastic-search to angular, go, machine learning, it has a total of 20 technologies with some recently added ones i.e. DAML, test automation, snowflake, and ionic.
How to upgrade your skills?
Every technology in Tech-hub has n number of templates. Once you click on any specific technology you’ll be able to see all the templates of that technology. Since these templates are downloadable, you need to provide your email to get the template downloadable link in your mail.
These templates helps you learn the practical implementation of a topic with so much of ease. Using these templates you can learn and kick-start your development in no time.
Apart from your learning, there are some out of the box templates, that can help provide the solution to your business problem that has all the basic dependencies/ implementations already plugged in. Tech hub names these templates as xlr8rs (pronounced as accelerators).
xlr8rs make your development real fast by just adding your core business logic to the template.
If you are looking for a template that’s not available, you can also request a template may be for learning or requesting for a solution to your business problem and tech-hub will connect with you to provide you the solution. Isn’t this helpful 🙂
Confused with which technology to start with?
To keep you updated, the Knoldus tech hub provides you with the information on the most trending technology and the most downloaded templates at present. This you’ll be informed and learn the one that’s most trending.
Since we believe:
“There’s always a scope of improvement“
If you still feel like it isn’t helping you in learning and development, you can provide your feedback in the feedback section in the bottom right corner of the website.
#ai #akka #akka-http #akka-streams #amazon ec2 #angular 6 #angular 9 #angular material #apache flink #apache kafka #apache spark #api testing #artificial intelligence #aws #aws services #big data and fast data #blockchain #css #daml #devops #elasticsearch #flink #functional programming #future #grpc #html #hybrid application development #ionic framework #java #java11 #kubernetes #lagom #microservices #ml # ai and data engineering #mlflow #mlops #mobile development #mongodb #non-blocking #nosql #play #play 2.4.x #play framework #python #react #reactive application #reactive architecture #reactive programming #rust #scala #scalatest #slick #software #spark #spring boot #sql #streaming #tech blogs #testing #user interface (ui) #web #web application #web designing #angular #coronavirus #daml #development #devops #elasticsearch #golang #ionic #java #kafka #knoldus #lagom #learn #machine learning #ml #pandemic #play framework #scala #skills #snowflake #spark streaming #techhub #technology #test automation #time management #upgrade
1594486620
How Change Data Capture (CDC) gets benefits from Delta Lake
Introduction
Enterprise has been spending millions of dollars getting data into data lakes using Apache Spark with the aspiration to perform Machine Learning and to build Recommendation engines, Fraud Detection, IoT & Predictive maintenance etc. But the fact is majority of these projects are failing in getting the reliable data.
Challenges with the traditional data lake
- Failed Production Jobs will leave the data in corrupted state and it requires tedious job to recover the data. We need to have some script to clean up and to revert the transaction.
- Lack of schema enforcement creates inconsistent data and low quality data.
- Lack of consistency, while reading the data when there is a concurrent write, result will not be inconsistent until Parquet is fully updated. When there is multiple writes happening in streaming job, the downstream apps reading this data will be inconsistent because there is no isolation between each writes.
“Delta Lake overcomes the above challenges”
Delta Lake
Databricks open sourced their proprietary storage name in the name of Delta Lake, to bring ACID transactions to Apache Spark and big data workloads. Earlier Delta lake is available in Azure/AWS Databricks only where the data will get stored only on DBFS, which may lie on top of ADLS/S3. Now Delta format can lie on HDFS, ADLS, S3 or local File system, etc…. Delta Lake is also compatible with MLFlow.
How Delta Works?
Delta lake is based on Parquet, it adds the transactional awareness to Parquet using transaction log which will be maintained in additional folder (_delta_log ) under the table directory. Lot of vendors like Informatica, Talend embrace delta and working on native readers and writers.
Json file under the _delta_log folder will have the information like add/remove parquet files(for Atomicity), stats(for optimized performance & data skipping), partitionBy(for partition pruning), readVersions(for time travel), commitInfo(for audit).
Below is the Json file which present in delta transactional log when we write sample DataFrame with 2 records. Notice that it analyse the status like min,max in each file which helps to effectively skips the unnecessary data and helps in performance optimization.
#spark #databricks #delta #delta-lake #big-data #data analysis