SDSS-V will be an all-sky, multi-epoch spectroscopic survey of over six million objects. It is designed to decode the history of the Milky Way Galaxy (MW), trace the emergence of the chemical elements, reveal the inner workings of stars, and investigate the origin of planets. It will also create an integral-field spectroscopic map of the interstellar gas in the Galaxy and the Local Group that is 1,000 times larger than the current state of the art and at high enough spatial resolution to reveal the self-regulation mechanisms of galactic ecosystems. SDSS-V will pioneer systematic, spectroscopic monitoring across the whole sky, revealing changes on timescales from 20 minutes to 20 years. The survey will thus track the flickers, flares, and radical transformations of the most luminous persistent objects in the universe: massive black holes growing at the centers of galaxies.The scope and flexibility of SDSS-V will be unique among both extant and anticipated spectroscopic surveys: it is all-sky, with matched survey infrastructures in both hemispheres; it provides near-infrared and optical multi-object fiber spectroscopy that is rapidly reconfigurable to serve high target densities, targets of opportunity, and time-domain monitoring; and it provides optical, ultrawide-field integral field spectroscopy. SDSS-V, with its programs anticipated to start in 2020, will be perfectly timed to multiply the scientific output from major space missions (e.g., TESS, Gaia, Spektr-RG-eROSITA) and ground-based projects. SDSS-V builds on the 25-year heritage of SDSS's advances in data analysis, collaboration spirit and infrastructure, and product deliverables in astronomy. The project is now refining its science scope, optimizing the survey strategies, and developing new hardware that builds on the SDSS-IV infrastructure. We present here an overview of the current state of these developments. SDSS-V is actively seeking to build its consortium of institutional and individual members for a worldwide, partner-driven collaboration.

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys.

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The next -generation astronomy digital archives will cover most of the sky at fine resolution in many wavelengths, from X-rays, through ultraviolet, optical, and infrared. The a rchives will be stored at diverse geographical locations. One of the first of these projects, the Sloan Digital Sky Survey (SDSS) is creating a 5 -wavelength catalog over 10,000 square degrees of the sky (see http://www.sdss.org/). The 200 million objects in the multi-terabyte database will have mostly numerical attributes in a 100+ dimensional space. Points in this space have highly correlated distributions.The archive will enable astronomers to explore the data interactively. Data access will be aided by multidimensional spatial and attribute indices. The data will be partitioned in many ways. Small tag objects consisting of the most popular attributes will accelerate frequent searches. Splitting the data among multiple servers will allow parallel, scalable I/O and parallel data analysis. Hashing techniques will allow efficient clustering, and pair-wise comparison algorithms that should parallelize nicely. Randomly sampled subsets will allow debugging otherwise large queries at the desktop. Central servers will operate a data pump to support sweep searches touching most of the data. The anticipated queries will require special operators related to angular distances and complex similarity tests of object properties, like shapes, colors, velocity vectors, or temporal behaviors. These issues pose interesting data management challenges.

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