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These are the 2 fastest growing areas, and making them work together is a major goal for Databricks. In a way, Delta represents the direction and philosophy of Databricks and its founders perfectly. You probably did not hear it here first. Why should students think about going to college? Much like what the situation was in Hadoop a few years ago.
Published by Emmanuel Radcliff Modified over 4 years ago. Vancouver HS and Mt. Vernon HS developed lesson plans in Updated in Designed to supplement Navigation Sparking the Future was developed as part of a generous grant from the College Spark Foundation. It was designed to provide extra support for students who are the first in their families to be considering college — to help them explore the reasons for college and then learn about what they need to do during high school in terms of both high school coursework and college applications to be ready to succeed in college.
The Sparking the Future lessons were developed on the ground during the school year, by teams from Ft. Vancouver High School and Mt. The lessons were extensively updated during fall to incorporate new information and resources and to align the lessons with the Common Core State Standards. Sparking the Future lessons were designed by educators who to supplement Navigation These lessons will provide extra support and targeted assistance to students who are considering college… but are unsure what it will take to get there.
The goals of the program are to: Help students understand the importance of college, by learning about the educational requirements of careers that interest them and by learning about the economic and earnings implications of a college degree. Help students explore a range of college and career options to broaden their horizons and explicitly connect their interests and skills to potential career opportunities.
Give students the information and resources they need to prepare for, apply, and be competitive for admission to the college of their choice, whether that be a two-year community college, a technical college, or a four-year college or university. As I noted earlier, Sparking the Future was designed to focus specifically on students who will be the first in their family to attend college.
As such, it provides step-by-step and even month-by-month guidance to help students through what can be a challenging and confusing process. A set of 6 lessons for grades focus on self-exploration and the importance of college and help students understand the importance of the work they will be doing in high school, easing their transition from middle school to high school.
A set of 7 lessons for grades help students take a closer look at college and career options, and highlight the importance of the courses they take during their high school years, as well as the college entrance exams — from the SAT to the ASVAB — that they will need to take. A final set of 9 lessons for grades help students put the pieces together, by connecting career goals with college programs and then guiding students through the college and financial aid application milestones of junior and senior year.
Learning goals Alignment with standards Materials needed Activities Student products Additional resources Each lesson plan has same format To make Sparking the Future easy for teachers and counselors to use, all of the lesson plans are one to two pages in length and all of them use the same format. Each lesson plan includes information on: The goals for student learning, focused around the outcomes for students following successful completion of the lesson; Notes on how the lesson is aligned with academic and guidance standards; A list of the materials that will be needed including handouts, Internet access, or community resources ; A step-by-step guide through the activities students will complete during the lesson; The products students will complete including handouts, games, or essays ; and Additional resources advisors can pursue to help build on the lesson.
Lifestyle goals Interests and strengths College and career options Graduation and admission requirements Application process Promote self-reflection Demonstrate learning Most of the Sparking the Future lesson plans suggest exercises to help students absorb and reflect on what they have learned. In many cases, these student exercises are provided in the form of handouts that are included with the Sparking the Future lesson plan packets. Handouts provided with the lessons help students explore concepts covered in the lessons, including: The handouts are designed to help students reflect on their goals and on the work they have done to achieve their goals.
Why go to college? What occupations are growing? What do I need to graduate? What do I need for college? What do colleges want? Measure student readiness Many of the Sparking the Future handouts ask students to find the answers to factual questions. In these cases, the handouts are supplemented with answer keys for the advisor. The answer keys help advisors enrich their work with students. They give advisors the information they need to pursue subjects that include: Why should students think about going to college? What are the fastest growing occupations in our state?
What courses and how many credits will I need to graduate? What courses and how many credits will I need to be ready for college? What do colleges want from students in their applications? Education after high school Knowing yourself Career interests Help with transition to high school GRADES Sparking the Future for grades includes six lessons that focus on the importance of college. The lesson plans are based around three themes: Education after high school Knowing yourself Career interests Each of these themes is based around self-discovery and interest assessment… to help students see how their interests and strengths can shape their path to the future.
Students assess their own lifestyle goals and then explore the educational requirements for their career goals. They learn basic facts about college and family wage jobs and begin to understand the connection between education and income. And they learn about the importance of the work they will do during high school to their college and career options.
Each of these lessons includes a lighthearted quiz to help students assess their college and career readiness. Interests and aptitudes The connection between interests and careers Character traits Who am I? Who do I want to be? These lessons help students explore who they are and who they want to be… and then connect their interests to career and educational opportunities. Making the connection between interests and potential careers is particularly important for students at this age level. It gives them something tangible to work for, helping them understand why the work they do today matters for the future.
During lessons 5 and 6, students learn how to explore career possibilities and are introduced to resources they can use to learn more about careers. They also explore high-growth occupations and learn what types of jobs are most in demand. Ghodsi says that this has been their intent with Databricks all along, citing shorter iterations as a major reason:. So it's all cloud for Databricks, which also happens to be in the driver's seat of the most popular big data platform at the moment. Spark is comprised of many elements brought together to assist teams working with data to extract insights.
It's not the only one of course. To begin with, there is competition from cloud providers, each of which offers core Spark in addition to its own tools for working with data in the cloud. While tempting for organizations that already have a footprint in these clouds, such offerings also entail lock-in and are not necessarily best of breed. Choosing an insight platform will be the next great platform decision, and other vendors are in this game as well.
How do Databricks founders compare themselves against other options?
This is not something we do, not interested in that, and we don't see them in deals that much. And what about Hadoop and its key vendors?
They were not included in Forrester's evaluation or our conversation, but arguably may be part of the insight platform decision. It's not so much about Hadoop anymore , but more about what you can do with it. Hadoop vendors are aware of this apparently and going the IPaaS way, each at its own pace and in its own fashion. Then there are some less obvious, also not included in analysts reports in that space, options. Confluent has recently added its own managed cloud version of Kafka, and in addition is expanding its reach and ambitions.
Kafka is the most popular entry point for streaming architectures, and Confluent wants to grow Kafka into a platform to build micro services on. It's also adding features such as transformations and SQL on streaming data. Ghodsi and Zaharia point out that while there is some overlap, Spark is all about integration: But Kafka's transformation and SQL can only take you so far.
If you want to do batch and streaming data, and some machine learning on top, as most of our clients do, our integrated tools and APIs is what you need". Who really owns your Internet of Things data? In a world where more and more objects are coming online and vendors are getting involved in the supply chain, how can you keep track of what's yours and what's not?
What if streaming is your main concern though? Should you go for Spark Structured Streaming , or the portability and flexibility that Apache Beam promises? Beam is a project started by Google and donated to Apache, with the goal of acting as in interoperability layer for streaming engines such as Google's Dataflow, Flink or Spark. Google's Tyler Akidau, who is also Apache Beam's mastermind, mentioned in a talk a few months back that this was left to the Spark community. Confluent's CEO also said they are not interested in Beam unless it adds support for tables. There seems to be a bit of a stalemate there, and Spark's PMC does not seem to think there is much value in going for Beam compatibility at this point.
Zaharia says they tried to come up with an easier to use version of Beam by keeping the key ideas in Beam, which are separating query and trigger, but making them independent:. Spark structured streaming uses Spark's SQL engine under the hood, and for Zaharia this is what makes the difference:. Zaharia also referred to some benchmarks comparing Spark Structured Streaming against Kafka and Flink. He says they used the same configuration used by Kafka and Flink, four times fewer nodes, and the difference in performance was staggering: This is what we tried to optimize.
Databricks ran the Yahoo benchmark for streaming to compare Spark Structured Streaming against Kafka and Flink and got superior results using less nodes. Ghodsi says that using the skipping capabilities you could do point SQL queries. Although using Spark as the back-end for your web store for example is not something Databricks would normally recommend, some people are doing that. What Databricks would recommend however is to use Spark Structured Streaming combined with a transactional database, to do things such as keeping calculated statistics up to date.
Zaharia commends on SpliceMachine's approach, noting that while there also are other approaches that try to bypass the immutability aspect of Spark's core data structure RDD , SnappyData took the extra effort to integrate Spark with a transactional store on a low level.
Zaharia also confirms that some of SnappyData's solutions on indexing for example have been adopted by core Spark too. So where is Spark, and Databricks, headed next?
Zaharia says the bigest thing they are currently working on is streaming and deep learning DL. These are the 2 fastest growing areas, and making them work together is a major goal for Databricks. He says the main reason things are complicated is that the tools are new, and they are not that well integrated. Much like what the situation was in Hadoop a few years ago.
But to build an application you needed to combine four or five different systems.
The key thing you need in programming is composition. It's OK to implement 1 MapReduce, but in most algorithms you would need over In Spark you just use functions, you don't even need to know what the operators inside them are.
Now, if your execution engine knows how to do both loading data and training the algorithms, then you don't need separate systems. This is what we are doing. We have one API through which you can do batch, streaming and joins, and this simplifies things.
We are now adding DL in our ML pipeline. It turns out if you add DL operators there, many common use cases are easy to implement. If you look at DL frameworks like TensorFlow, they are designed for people developing new models. For our pipelines, we are focused on people who want to use existing models and train them on their data to apply to their problem. In our demo we built a state of the art visual search in 7 lines of code. People want to build end to end applications quickly and what matters most is to be able to compose applications out of little pieces easily and efficiently.